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Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics - Tutorial at ICTIR 2016

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UPMC - Sorbonne Universities
UPMC - Sorbonne Universities

A great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-user collaboration is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex search tasks.This tutorial will first give an overview of the ways into collaboration has been implemented in IR models with the attempt of improving the search outcomes with respect to several tasks and related frameworks (ad-hoc search, group-based recommendation, social search, collaborative search). Second, as envisioned in collaborative IR domain (CIR), we will focus on the theoretical models that support and drive user-to-user collaboration in order to perform shared IR tasks. Third, we will develop a road map on emerging and relevant topics addressing issues related to collaboration design. Our goal is to provide participants with concepts and motivation allowing them to investigate this emerging IR domain as well as giving them some clues on how to tackle issues related to the optimization of collaborative tasks. More specifically, the tutorial aims to: 1. Give an overview of the key concept of collaboration in IR and related research topics; 2. Present state-of-the art CIR techniques and models; 3. Discuss about the emerging topics that deal with collaboration; 4. Point out some challenges ahead.

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1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics Lynda Tamine Paul Sabatier University IRIT, Toulouse - France Laure Soulier Pierre and Marie Curie University LIP6, Paris - France Monday 17th October, 2016 1 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion GOAL OF THE TUTORIAL • Introducing the notion of collaboration and the different forms of collaborative information retrieval and seeking Positioning collaborative IR within the major theoretical approaches of IR Identifying the Collaborative IR challenges • Presenting state-of-the-art theoretical models for collaborative IR Identifying the key factors affecting the design of collaborative IR models Reviewing major research progress in the area • Discussing promising research directions Bridging the gap between two (close) research branches: collaborative IR and social media IR 2 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OUTLINE OF THE TUTORIAL • Part 1: Collaboration in information seeking and retrieval • Part 2: Models and techniques for document seeking and retrieval • Part 3: Emerging topics around collaboration • Part 4: Discussion 3 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR What does collaboration refer to (in IR)? Collaborative information retrieval paradigms Collaborative information retrieval challenges and issues 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 4 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE NOTION OF COLLABORATION Collaboration “A process through which parties who see different aspects of a problem can constructively explore their differences and search for solutions that go beyond their own limited vision of what is possible.” [Gray, 1989] Collaboration “Collaboration is a process in which autonomous actors interact through formal and informal negotiation, jointly creating rules and structures governing their relationships and ways to act or decide on the issues that brought them together; it is a process involving shared norms and mutually beneficial interactions.” [Thomson and Perry, 2006] Collaborative information seeking and retrieval “The study of the systems and practices that enable individuals to collaborate during the seeking, searching, and retrieval of information.” [Foster, 2006] 5 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration • User-user (and user-system) collaboration 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. • User-user (and user-system) collaboration 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. • User-user (and user-system) collaboration 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. • User-user (and user-system) collaboration 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. Why? Ensuring long-term search gain and/or synergic effect 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. Why? Ensuring long-term search gain and/or synergic effect How? Exploiting relevance feedback, using the group members’ social interactions, personal and evolving behavioral data 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. Why? Ensuring long-term search gain and/or synergic effect How? Exploiting relevance feedback, using the group members’ social interactions, personal and evolving behavioral data Main IR research branches: Social media IR [Evans and Chi, 2008, Horowitz and Kamvar, 2010], collaborative filtering [Sarwar et al., 2001], collaborative IR [Shah et al., 2010, Soulier et al., 2014b] 6 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: Static IR: system-based IR, does not learn from users eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank and Hits [Brin and Page, 1998] 7 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: Static IR: system-based IR, does not learn from users eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank and Hits [Brin and Page, 1998] Interactive IR: exploiting feedback from users eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001] 7 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: Static IR: system-based IR, does not learn from users eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank and Hits [Brin and Page, 1998] Interactive IR: exploiting feedback from users eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001] Dynamic IR: learning dynamically from past user-system interactions and predicts future eg. iPRP [Fuhr, 2008], interactive exploratory search [Jin et al., 2013] 7 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: 8 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: 8 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION The social collaborative IR dimensions [Golovchinsky et al., 2009]: • Intent: explicit vs. implicit search goal • Depth of mediation: interface vs. algorithms (system) • Concurrency: synchronous vs. asynchronous • Location: co-located vs. remote 9 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Main IR research branches involving user-user collaboration Collaborative IR Social media IR Collaborative filtering Intent Explicit Implicit Implicit Depth of mediation Interface/Algorithms Algorithms Algorithms Concurrency Synchronous/ Asynchronous Asynchronous Asynchronous Location Co-located/ Re- mote Remote Remote 10 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users 11 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users • Collaborative filtering [Resnick et al., 1994, Ma et al., 2009] Recommending search results using ratings/preferences of other users How? Inferring user’s own preferences from other users’ preferences Personalizing search results 11 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users • Collaborative filtering [Resnick et al., 1994, Ma et al., 2009] Recommending search results using ratings/preferences of other users How? Inferring user’s own preferences from other users’ preferences Personalizing search results • Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011] Exploiting social media platforms to retrieve document/users... How? Social network analysis (graph structure, information diffusion, ...) Integrating social-based features within the document relevance scoring 11 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users • Collaborative filtering [Resnick et al., 1994, Ma et al., 2009] Recommending search results using ratings/preferences of other users How? Inferring user’s own preferences from other users’ preferences Personalizing search results • Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011] Exploiting social media platforms to retrieve document/users... How? Social network analysis (graph structure, information diffusion, ...) Integrating social-based features within the document relevance scoring Let’s have a more in-depth look on... Collaborative Information Retrieval 11 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION 12 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search 13 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor 13 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities 13 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities When? Synchronous vs. Asynchronous 13 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities When? Synchronous vs. Asynchronous Where? Colocated vs. Remote 13 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities When? Synchronous vs. Asynchronous Where? Colocated vs. Remote How? Crowdsourcing Implicit vs. Explicit intent User mediation System mediation 13 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion CIR PARADIGMS [FOLEY AND SMEATON, 2010, KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010] Division of labor • Role-based division of labor • Document-based division of labor 14 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion CIR PARADIGMS [FOLEY AND SMEATON, 2010, KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010] Division of labor • Role-based division of labor • Document-based division of labor Sharing of knowledge • Communication and shared workspace • Ranking based on relevance judgements 14 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion CIR PARADIGMS [FOLEY AND SMEATON, 2010, KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010] Division of labor • Role-based division of labor • Document-based division of labor Sharing of knowledge • Communication and shared workspace • Ranking based on relevance judgements Awareness • Collaborators’ actions • Collaborators’ context 14 / 102
Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion THEORETICAL CHALLENGES Typical structure of a collaborative search session Challenges and issues 1 Learning from user and user-user past interactions 2 Adaptation to multi-faceted and multi-user contexts: skills, expertise, role, etc. 3 Aggregating relevant information nuggets 4 Supporting synchronous vs. asynchronous coordination 5 Modeling collaboration paradigms: division of labor, sharing of knowledge 6 Optimizing the search cost: balance in work (search) and group benefit (task outcome) 15 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Understanding Collaborative IR Overview System-mediated CIR models User-Driven System-mediated CIR models Roadmap 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 16 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Objectives 1 Investigating user behavior and search patterns Search processes [Shah and Gonz´alez-Ib´a˜nez, 2010, Yue et al., 2014] Search tactics and practices [Hansen and J¨arvelin, 2005, Morris, 2013, Amershi and Morris, 2008, Tao and Tombros, 2013, Capra, 2013] Role assignment [Imazu et al., 2011, Tamine and Soulier, 2015] 2 Studying the impact of collaborative search settings on performance Impact of collaboration on search performance [Shah and Gonz´alez-Ib´a˜nez, 2011, Gonz´alez-Ib´a˜nez et al., 2013] 17 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • Study objective: Testing the feasibility of the Kuhlthau’s model of the information seeking process in a collaborative information seeking situation [Shah and Gonz´alez-Ib´a˜nez, 2010] Stage Feeling Thoughts Actions (Affective) (Cognitive) Initiation Uncertainty General/Vague Actions Selection Optimism Exploration Confusion, Frustration, Doubt Seeking relevant informa- tion Formulation Clarity Narrowed, Clearer Collection Sense of direction, Confidence Increased interest Seeking relevant or focused information Presentation Relief, Satisfaction or disap- pointment Clearer or focused 18 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • Study objective: Testing the feasibility of the Kuhlthau’s model in collaborative information seeking situations [Shah and Gonz´alez-Ib´a˜nez, 2010] Participants: 42 dyads, students or university employees who already did a collaborative work together System: Coagmento 1 Sessions: two sessions (S1, S2) running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: simulated work tasks. eg. Task 1: Economic recession ”A leading newspaper has hired your team to create a comprehensive report on the causes and consequences of the current economic recession in the US. As a part of your contract, you are required to collect all the relevant information from any available online sources that you can find. ... Your report on this topic should address the following issues: reasons behind this recession, effects on some major areas, such as health-care, home ownership, and financial sector (stock market), unemployment statistics over a period of time, proposal execution, and effects of the economy simulation plan, and people’s opinions and reactions on economy’s downfall” 1 http://www.coagmento.org/ 19 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • (Main) Study results: The Kuhlthau’s model stages map collaborative tasks • Initiation: number of chat messages at the stage and between stages • Selection: number of chat messages discussing the strategy • Exploration: number of search queries • Formulation: number of visited webpages • Collection: number of collected webpages • Presentation: number of moving actions for organizing collected snippets Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2010] 20 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • (Main) Study results: The Kuhlthau’s model stages map collaborative tasks • Initiation: number of chat messages at the stage and between stages • Selection: number of chat messages discussing the strategy • Exploration: number of search queries • Formulation: number of visited webpages • Collection: number of collected webpages • Presentation: number of moving actions for organizing collected snippets Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2010] 20 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING SEARCH TACTICS AND PRACTICES • Study objective: Analyzing query (re)formulations and related term sources based on participants’ actions [Yue et al., 2014] Participants: 20 dyads, students who already knew each other in advance System: Collabsearch Session: one session running in running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: (T1) academic literature search, (T2) travel planning 21 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING SEARCH TACTICS AND PRACTICES • (Main) Study results: Individual action-based query reformulation (V, S, Q): No (significant) new findings Collaborative action-based query reformulation (SP, QP, C): Influence of communication (C) is task-dependent. Influence of collaborators’ queries (QP) is significantly higher than previous own queries (Q). Less influence of collaborators’ workspace (SP) than own workspace (S). • V: percentage of queries for which participants viewed results, one term originated from at least one page • S: percentage of queries for which participants saved results, one term originated from at least one page • Q: percentage of queries with at least one overlapping term with previous queries • SP: percentage of queries for which at least one term originated from collaborators’ workspace • QP: percentage of queries for which at least one term originated from collaborators’ previous queries • C: percentage of queries for which at least one term originated from collaborators’ communication Figure: c [Yue et al., 2014] 22 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: STUDYING ROLE ASSIGNMENT • Study objective: Understanding differences in users’ behavior in role-oriented and non-role- oriented collaborative search sessions Participants: 75 dyads, students who already knew each other Settings: 25 dyads without roles, 50 dyads with roles (25 PM roles, 25 GS roles) System: open-source Coagmento plugin Session: one session running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: Three (3) exploratory search tasks, topics from Interactive TREC track2 Tamine, L. and Soulier, L. (2015). Understanding the impact of the role factor in collaborative information retrieval. In Proceedings of the ACM International on Conference on Information and Knowledge Management, CIKM 15, pages 4352. 2 http://trec.nist.gov/data/t8i/t8i.html 23 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: STUDYING ROLE ASSIGNMENT • (Main) Study results Users with assigned roles significantly behave differently than users with roles Mean(s.d.) npq dt nf qn ql qo nbm W/Role GS Group 1.71(1.06) 9.99(3.37) 58.52(27.13) 65.91(31.54) 4.64(1.11) 0.44(0.18) 20(14.50) IGDiffp -0.52 -3.47*** 1.30*** 2.09*** 1.16*** 0.14*** 2.23*** PM Group 1.88(1.53) 10.47(3.11) 56.31(27.95) 56.31(27.95) 2.79(0.70) 0.39(0.08) 15(12.88) IGDiffp 0.24*** 1.45*** -2.42*** -1.69*** 0.06*** 0-0.23*** 0.05*** W/oRole Group 2.09(1.01) 13.16(3.92) 24.13(12.81) 43.58(16.28) 3.67(0.67) 0.45(0.10) 19(11.34) p-value/GS *** *** *** *** *** *** p-value/PM *** *** *** *** *** *** * W/Role vs. W/oRole ANOVA p-val. ** *** ** * 24 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: STUDYING ROLE ASSIGNMENT • (Main) Study results Early and high level of coordination of participants without role Role drift for participants with PM role (a) GS (b) PM (c) W/oRole 25 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE • Study objective: Evaluating the synergic effect of collaboration in information seeking [Shah and Gonz´alez-Ib´a˜nez, 2011] Participants: 70 participants, 10 as single users, 30 as dyads Settings: C1 (single users), C2 (artificial formed teams), C3 (co-located teams, different computers), C4 (co-located teams, same computer), C5 remotely located teams System: Coagmento Session: one session running in running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: One exploratory search task, topic ”gulf oil spill” 26 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE • (Main) Study results Value of remote collaboration when the task has clear independent components Remotely located teams able to leverage real interactions leading to synergic collaboration Cognitive load in a collaborative setting not significantly higher than in an individual one Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2011] 27 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques • Back to the axiomatic relevance hypothesis (Fang et al. 2011) 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques • Back to the axiomatic relevance hypothesis (Fang et al. 2011) • Role as a novel variable in the IR models ? 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques • Back to the axiomatic relevance hypothesis (Fang et al. 2011) • Role as a novel variable in the IR models ? • Learning to rank from user-system, user-user interactions within multi-session search tasks? 28 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA 29 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA 29 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES Collaborative IR models are based on algorithmic mediation: Systems re-use users’ search activity data to mediate the search • Data? Click-through data, queries, viewed results, result rankings, ... User-user communication • Mediation? Rooting/suggesting/enhance the queries Building personalized document rankings Automatically set-up division of labor 30 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES Collaborative IR models are based on algorithmic mediation: Systems re-use users’ search activity data to mediate the search • Data? Click-through data, queries, viewed results, result rankings, ... User-user communication • Mediation? Rooting/suggesting/enhance the queries Building personalized document rankings Automatically set-up division of labor 30 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES Notations Notation Description d Document q Query uj User j g Collaborative group ti term i RSV(d, q) Relevance Status Value given (d,q) N Document collection size ni Number of documents in the collection in which term ti occurs R Number of relevant documents in the collection Ruj Number of relevant documents in the collection for user uj r uj i Number of relevant documents of user uj in which term ti occurs 31 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER GROUP-BASED MEDIATION • Enhancing collaborative search with users’ context [Morris et al., 2008, Foley and Smeaton, 2009a, Han et al., 2016] Division of labor: dividing the work by non-overlapping browsing Sharing of knowledge: exploiting personal relevance judgments, user’s authority 32 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: GROUPIZATION, SMART SPLITTING, GROUP-HIGHLIGHTING [MORRIS ET AL., 2008] • Hypothesis setting: one or a few synchronous search query(ies) • 3 approaches Smart splitting: splitting top ranked web results using a round-robin technique, personalized-splitting of remaining results (document ranking level) Groupization: reusing individual personalization techniques towards groups (document ranking level) Hit Highlighting: highlighting user’s keywords (document browsing level) 33 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008] Personalizing the document ranking: use the revisited BM25 weighting scheme [Teevan et al., 2005] RSV(d, q, uj) = ti∈d∩q wBM25(ti, uj) (1) wBM25(ti, uj) = log (ri + 0.5)(N − ni − Ruj + r uj i + 0.5) (ni − r uj i + 0.5)(Ruj − r uj i + 0.5 (2) N = (N + Ruj ) (3) ni = ni + r uj i (4) 34 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008] Example Smart-splitting according to personalized scores. 35 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B] • Hypothesis setting: multiple independent synchronous search queries • Collaborative relevance feedback: sharing collaborator’s explicit relevance judgments Aggregate the partial user relevance scores Compute the user’s authority weighting Figure: c [Foley et al., 2008, Foley and Smeaton, 2009b] 36 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B] • A: Combining inputs of the RF process puwo(ti) = U−1 u=0 ruiwBM25(ti) (5) wBM25(ti) = log ( U−1 u=0 αu ru i Ru )(1 − U−1 u=0 αu ni − rui N − Ru ) ( U−1 u=0 αu ni − rui N − Ru )(1 − U−1 u=0 αu rui Ru ) (6) U−1 u=0 αu = 1 (7) 37 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B] • B: Combining outputs of the RF process crwo(ti) = U−1 u=0 αuwBM25(ti, u) (8) wBM25(ti, u) = log ( ru i Ru )(1 − ni − rui N − Ru ) ( ni − rui N − Ru )(1 − rui Ru ) (9) • C: Combining outputs of the ranking process RSV(d, q) = U−1 u=0 αuRSV(d, q, u) (10) RSV(d, q, u) = ti∈d∩q wBM25(ti, u) (11) 38 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016] • Exploit a 3-dimensional context: Individual search history HQU: queries, results, bookmarks etc.) Collaborative group HCL: collaborators’ search history (queries, results, bookmarks etc.) Collaboration HCH: collaboration behavior chat (communication) Figure: c [Han et al., 2016] 39 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016] 1 Building a document ranking RSV(q, d) and generating Rank(d) 2 Building the document language model θd 3 Building the context language model θHx p(ti|Hx) = 1 K K k=1 p(ti|Xk) (12) p(ti|Xk) = nk Xk (13) 4 Computing the KL-divergence between θHx and θd D(θd, θHx ) = − ti p(ti|θd) log p(ti|Hx) (14) 5 Learning to rank using pairwise features (Rank(d), D(θd, θHx)) 40 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION Enhancing collaborative search with user’s role [Pickens et al., 2008, Shah et al., 2010, Soulier et al., 2014b] • Division of labour: dividing the work based on users’ role peculiarities • Sharing of knowledge: splitting the search results 41 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008] • Prospector/Miner as functional roles supported by algorithms: Prospector: ”..opens new fields for exploration into a data collection..”. → Draws ideas from algorithmically suggested query terms Miner: ”..ensures that rich veins of information are explored...”. → Refines the search by judging highly ranked (unseen) documents • Collaborative system architecture: Algorithmic layer: functions combining users’ search activities to produce fitted outcomes to roles (queries, document rankings). Regulator layer: captures inputs (search activities), calls the appropriate functions of the algorithmic layer, roots the outputs of the algorithmic layer to the appropriate role (user). Figure: c [Pickens et al., 2008] 42 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008] • Prospector function: The highly-relevant terms are suggested based on: Score(ti) = Lq∈L wr(Lq)wf (Lq)rlf(ti; Lq) (15) rlf(ti; Lq): number of documents in Lq in which ti occurs. • Miner function: The unseen documents are queued according to RSV(q, d) = Lq∈L wr(Lk)wf (Lq)borda(d; Lq) (16) wr(Lq) = |seen ∈ Lq| |seen ∈ Lq| (17) wf (Lq) = |rel ∈ Lq| |seen ∈ Lq| (18) 43 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: GATHERER AND SURVEYOR [SHAH ET AL., 2010] • Gatherer/Surveyor as functional roles supported by algorithms: Gatherer: ”..scan results of joint search activity to discover most immediately relevant documents..”. Surveyor: ”..browse a wider diversity of information to get a better understanding of the collection being searched...”. • Main functions: Merging: merging (eg. CombSum) the documents rankings of collaborators Splitting: rooting the appropriate documents according to roles (eg. k-means clustering). High precision for the Gatherer, high diversity for the Surveyor Figure: c [Shah et al., 2010] 44 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE Domain expert/Domain novice as knowledge-based roles supported by algorithms: • Domain expert: ”..represent problems at deep structural levels and are generally interested in discovering new associations among different aspects of items, or in delineating the advances in a research focus surrounding the query topic..”. • Domain novice: ”..represent problems in terms of surface or superficial aspects and are generally interested in enhancing their learning about the general query topic..”. Soulier, L., Tamine, L., and Bahsoun, W. (2014b). On domain expertise-based roles in collaborative information retrieval. Information Processing & Management (IP&M), 50(5):752774. 45 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] A two step algorithm: 1 Role-based document relevance scoring Pk (d|uj, q) ∝ Pk(uj|d) · Pk(d|q) (19) P(q|θd) ∝ (ti,wiq)∈q[λP(ti|θd) + (1 − λ)P(ti|θC)]wiq (20) Pk (uj|d) ∝ P(π(uj)k|θd) ∝ (ti,wk ij )∈π(uj)k [λk dj P(ti|θd) + (1 − λk dj )P(ti|θC)] wk ij (21) 46 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] A two step algorithm: 1 Role-based document relevance scoring : parameter smoothing using evidence from novelty and specificity λk dj = Nov(d, D(uj)k) · Spec(d)β maxd ∈D Nov(d, D(uj)k) · Spec(d )β (22) with β 1 if uj is an expert −1 if uj is a novice Novelty Nov(d, D(uj) k ) = mind ∈D(uj)k d(d, d ) (23) Specificity Spec(d) = avgti∈dspec(ti) = avgti∈d( −log( fdti N ) α ) (24) 47 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] A two step algorithm: 1 Document allocation to collaborators Classification-based on the Expectation Maximization algorithm (EM) E-step: Document probability of belonging to collaborator’s class P(Rj = 1|x k dj) = αk j · φk j (xk dj) αk j · φk j (xk dj ) + (1 − αk j ) · ψk j (xk dj ) (25) M-step : Parameter updating and likelihood estimation Document allocation to collaborators by comparison of document ranks within collaborators’ lists r k jj (d, δ k j , δ k j ) = 1 if rank(d, δk j ) < rank(d, δk j ) 0 otherwise (26) Division of labor: displaying distinct document lists between collaborators 48 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model Let’s consider: • A collaborative search session with two users u1 (expert) and u2 (novice). • A shared information need I modeled through a query q. • A collection of 10 documents and their associated relevance score with respect to the shared information need I. t1 t2 t3 t4 q 1 0 1 0 d1 2 3 1 1 d2 0 0 5 3 d3 2 1 7 6 d4 4 1 0 0 d5 2 0 0 0 d6 3 0 0 0 d7 7 1 1 1 d8 3 3 3 3 d9 1 4 5 0 d10 0 0 4 0 Weighting vectors of documents and query: q = (0.5, 0, 0.5, 0) ; d1 = (0.29, 0.43, 0.14, 0.14) d2 = (0, 0, 0.63, 0.37) d3 = (0.12, 0.06, 0.44, 0.28) d4 = (0.8, 0.2, 0, 0) d5 = (1, 0, 0, 0) d6 = (0.3, 0, 0, 0.7) d7 = (0.7, 0.1, 0.1, 0.1) d8 = (0.25, 0.25, 0.25, 0.25) d9 = (0.1, 0.4, 0.5, 0) d10 = (0, 0, 1, 0). Users profile: π(u1)0 = π(u2)0 = q 49 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model RSV(q, d) rank(d) Spec(d) d1 0.24 2 0.19 d2 0.02 7 0.23 d3 0.17 3 0.19 d4 0.03 6 0.15 d5 0.01 9 0.1 d6 0.02 8 0.1 d7 0.10 4 0.19 d8 0.31 1 0.19 d9 0.09 5 0.16 d10 0.01 10 0.15 • Iteration 0: Distributing top (6) documents to users: 3 most specific to the expert and the 3 less specific to the novice. Expert u1: l0 (u1, D0 ns) = {d8, d1, d3} Novice u2: l0 (u2, D0 ns) = {d7, d9, d4} 50 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model • Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4}). Building the user’s profile. π(u1)1 = (0.5, 0, 0.5, 0) π(u2)1 = ( 0.5+0.8 2 , 0.2 2 , 0.5 2 , 0) = (0.65, 0.1, 0.25, 0). Estimating the document relevance with respect to collaborators. For user u1 : P1 (d1|u1) = P1 (d1|q) ∗ P1 (u1|d1) = 0.24 ∗ 0.22 = 0.05. P1 (d1|q) = 0.24. P1 (u1|d1) = (0.85 ∗ 2 7 + 0.15 ∗ 24 84 )0.05 + (0.85 ∗ 3 7 + 0.15 ∗ 13 84 )0 + (0.85 ∗ 1 7 + 0.15 ∗ 26 84 )0.05 + (0.85 ∗ 1 7 + 0.15 ∗ 21 84 )0 = 0.22 λ1 11 = 1∗0.19 0.23 = 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document novelty score, and 0.23 the normalization score. The normalized document scores for each collaborators are the following: P1 (d|u1) P2 (d|u2) d1 0.23 0.28 d2 0 0.03 d3 0.16 0.11 d5 0.01 0.01 d6 0.03 0.02 d7 0.12 0.14 d8 0.34 0.34 d9 0.10 0.06 d10 0.01 0.01 51 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model • Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4, d5}). Building the user’s profile. π(u1)1 = (0.5, 0, 0.5, 0) π(u2)1 = ( 0.5+0.8 2 , 0.2 2 , 0.5 2 , 0) = (0.65, 0.1, 0.25, 0). Estimating the document relevance with respect to collaborators. For user u1 : P1 (d1|u1) = P1 (d1|q) ∗ P1 (u1|d1) = 0.24 ∗ 0.22 = 0.05. P1 (d1|q) = 0.24 since that the user’s profile has not evolve. λ1 11 = 1∗0.19 0.23 = 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document novelty score, and 0.23 the normalization score. P1 (u1|d1) = (0.85 ∗ 2 7 + 0.15 ∗ 24 84 )0.05 + (0.85 ∗ 3 7 + 0.15 ∗ 13 84 )0 + (0.85 ∗ 1 7 + 0.15 ∗ 26 84 )0.05 + (0.85 ∗ 1 7 + 0.15 ∗ 21 84 )0 = 0.22 The normalized document scores for each collaborators are the following: P1 (d|u1) P2 (d|u2) d1 0.23 0.28 d2 0 0.03 d3 0.16 0.11 d5 0.01 0.01 d6 0.03 0.02 d7 0.12 0.14 d8 0.34 0.34 d9 0.10 0.06 d10 0.01 0.01 52 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH Soulier, L., Shah, C., and Tamine, L. (2014a). User-driven System-mediated Collaborative Information Retrieval. In Proceedings of the Annual International SIGIR Conference on Research and Development in Information Retrieval, SIGIR 14, pages 485494. ACM. 53 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] • Identifying users’ search behavior differences: estimating significance of differences using the Kolmogrov-Smirnov test • Characterizing users’ role 54 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] • User’s roles modeled through patterns Intuition Number of visited documents Number of submitted queries Negative correlation Role pattern PR1,2 Search feature kernel KR1,2 Search feature-based correlation matrix FR1,2 F R1,2 =    1 if positively correlated −1 if negatively correlated 0 otherwise 55 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] • Categorizing users’ roles Ru argmin R1,2 ||FR1,2 C (tl) u1,u2 || (27) subject to : ∀ (fj,fk)∈K R1,2 FR1,2 (fj, fk) − C (tl) u1,u2 (fj, fk)) > −1 where defined as: FR1,2 (fj, fk) C (tl) u1,u2 (fj, fk) = FR1,2 (fj, fk) − C (tl) u1,u2 (fj, fk) if FR1,2 (fj, fk) ∈ {−1; 1} 0 otherwise • Personalizing the search: [Pickens et al., 2008, Shah, 2011]. 56 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators A collaborative search session implies two users u1 and u2 aiming at identifying information dealing with “global warming”. We present search actions of collaborators for the 5 first minutes of the session. u t actions additional information u2 0 submitted query “global warming” u1 1 submitted query “global warming” u2 8 document d1: visited comment: “interesting” u2 12 document d2: visited u2 17 document d3: visited rated: 4/5 u2 19 document d4: visited u1 30 submitted query “greenhouse effect” u1 60 submitted query “global warming definition” u1 63 document d20: visited rated: 3/5 u1 70 submitted query “global warming protection” u1 75 document d21: visited u2 100 document d5: visited rated: 5/5 u2 110 document d6: visited rated: 4/5 u2 120 document d7: visited u1 130 submitted query “gas emission” u1 132 document d22: visited rated: 4/5 u2 150 document d8: visited u2 160 document d9: visited u2 170 document d10: visited u2 200 document d11: visited comment: “great” u2 220 document d12: visited u2 240 document d13: visited u1 245 submitted query “global warming world protection” u1 250 submitted query “causes temperature changes” u1 298 submitted query “global warming world politics” 57 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators: matching with role patterns • Role patterns Roles of reader-querier F Rread,querier = 1 −1 −1 1 , K Rread,querier = {(Nq, Np)} Role : (S (tl) u1 , S (tl) u2 , Rread,querier) → {(reader, querier), (querier, reader)} (S (tl) u1 , S (tl) u2 , Rread,querier) → (reader, querier) if S (tl) u1 (tl, Np) > S (tl) u2 (tl, Np) (querier, reader) otherwise Role of judge-querier F Rjudge,querier = 1 −1 −1 1 , K Rjudge,querier = {(Nq, Nc)} Role : (S (tl) u1 , S (tl) u2 , Rjudge,querier → {(judge, querier), (querier, judge)} (S (tl) u1 , S (tl) u2 , Rjudge,querier) → (judge, querier) if S (tl) u1 (tl, Nc) > S (tl) u2 (tl, Nc) (querier, judge) otherwise 58 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators • Track users’ behavior each 60 seconds • F = {Nq, Nd, Nc, Nr}, respectively, number of queries, documents, comments, ratings. • Users’ search behavior S (300) u1 =      3 0 0 0 4 2 0 1 5 3 0 2 5 3 0 2 8 3 0 2      S (300) u2 =      1 4 1 1 1 7 1 3 1 10 1 3 1 13 2 3 1 13 2 3      • Collaborators’ search differences (matrix and Kolmogorov-Smirnov test) ∆ (300) u1,u2 =      2 −4 −1 −1 3 −5 −1 −2 4 −7 −1 −1 4 −10 −2 −1 7 −10 −2 −1      - Number of queries : p (tl) u1,u2 (Nq) = 0.01348 - Number of pages : p (tl) u1,u2 (Nd) = 0.01348 - Number of comments : p (tl) u1,u2 (Nc) = 0.01348 - Number of ratings : p (tl) u1,u2 (Nr) = 0.08152 59 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators: matching with role patterns • Collaborators’ search action complementarity: correlation matrix between search differences C (300) u1,u2 =    1 −0.8186713 −0.731925 0 −0.8186713 1 0.9211324 0 −0.731925 0.9211324 1 0 0 0 0 0    • Role mining: comparing the role pattern with the sub-matrix of collaborators’ behaviors Role of reader-querier ||F Rread,querier C (300) u1,u2 || = 0 −1 − (−0.8186713) −1 − (−0.8186713) 0 = 0 0.183287 0.183287 0 The Frobenius norm is equals to: √ 0.1832872 = 0.183287. Role of judge-querier ||F Rjudge,querier C (300) u1,u2 || = 0 −1 − (−0.731925) −1 − (−0.731925) 0 = 0 0.268174 0.268174 0 The Frobenius norm is equals to: √ 0.2681742 = 0.268174. → Collaborators acts as reader/querier with u1 labeled as querier and u2 as reader (highest Np). 60 / 102
1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES [FoleyandSmeaton,2009a] [Morrisetal.,2008]“smart-splitting” [Morrisetal.,2008]“groupization” [Pickensetal.,2008] [Shahetal.,2010] [Soulieretal.,IP&M2014b] [Soulieretal.,SIGIR2014a] Relevance collective individual Evidence source feedback interest expertise behavior role Paradigm division of labor sharing of knowledge 61 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Research fields and key critical questions Social media-based collaborative information access Crowdsourcing 4. Open ideas 5. Discussion 62 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? 63 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? 63 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? Who are the winners? How much times? 63 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? Who are the winners? How much times? How do they win? 63 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? Who are the winners? How much times? How do they win? 63 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion RESEARCH FIELDS AND KEY CRITICAL QUESTIONS SOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING • Social media-based collaborative information access Seeking, answering, sharing, bookmarking, and spreading information Implicit or explicit intents (sharing, questioning, and/or answering) → Improving the search outcomes through social interactions • Crowdsourcing Solving a task according to constraints (budget, time, ...) Defining, budgeting, and allocating the task → Identifying the right group of workers Emerging issue How to leverage from the wisdom of crowds? 64 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team • Social media-based collaboration Leveraging from the ”wisdom of the crowd” Tasks: social question-answering, social search, real-time search Emerging needs • Understanding the cognitive behaviors of social users sharing, assessing and disseminating information within social medias in order to achieve shared tasks leading to collective and productive outcomes. • Designing of a theoretical framework for collaborative IR within social environments 65 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Communication during a natural disaster People sent more than 20 million Tweets about the storm between Oct 27 & Nov 1. Terms tracked: ”sandy”, ”hurricane”, #sandy, #hurricane. 66 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS 67 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS 67 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] Investigating the motivation of using social media for search tasks Analyzing information needs Studying the scope of social interactions Analyzing users’ satisfaction 68 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] Investigating the motivation of using social media for search tasks Analyzing information needs Studying the scope of social interactions Analyzing users’ satisfaction • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] 68 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms Research questions What are the structural and semantic patterns of explicit collaboration? How groups of users with similar or complementary interests may be more likely to explicitly collaborate with each other? 1 Hurricane #Sandy (October 2012) 2 #Ebola virus epidemic (2013-2014) Lynda Tamine, Laure Soulier, Lamjed Ben Jabeur, Frdric Amblard, Chihab Hanachi, Gilles Hubert, and Camille Roth. Social media-based collaborative information access: Analysis of online crisis-related twitter conversations. ACM conference on HyperText and hypermedia, 2016. 69 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree 70 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree Analyzing the patterns of collaboration networks 70 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree Analyzing the patterns of collaboration networks Extracting collaboration topics through the LDA algorithm Sandy: Insults; Prayers; Negative thoughts; Thanks Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people 70 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree Analyzing the patterns of collaboration networks Extracting collaboration topics through the LDA algorithm Sandy: Insults; Prayers; Negative thoughts; Thanks Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people Building the social-collaboration network over the whole graph 70 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] 71 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] Limitations of social media information access Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. 71 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] Limitations of social media information access Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. Design implications • Recommendation of collaborators (asking questions to crowd instead of followers) • Enhancement of social awareness (creating social ties to active users) 71 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS THE APPROACHES: MEDIATION AT THE USER LEVEL • Recommending users Expertise and interests [Pal and Counts, 2011, Balog et al., 2012, Ghosh, 2012, Bozzon et al., 2012, Hecht et al., 2012, Wang et al., 2013, Gong et al., 2015, Ranganath et al., 2015] Social availability/Responsiveness [Horowitz and Kamvar, 2010, Sung et al., 2013, Ranganath et al., 2015] Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013, Ranganath et al., 2015] Users’ connectedness [Horowitz and Kamvar, 2010] • Identifying the right group of collaborators Expertise and interests [Chang and Pal, 2013, Nushi et al., 2015, Ranganath et al., 2015, Soulier et al., 2016] Social availability/Responsiveness [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015] Social activity [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015, Ranganath et al., 2015, Soulier et al., 2016] Users’ connectedness [Ranganath et al., 2015] Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013] Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016] Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016] 72 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS THE APPROACHES: MEDIATION AT THE USER LEVEL • Recommending users Expertise and interests [Pal and Counts, 2011, Balog et al., 2012, Ghosh, 2012, Bozzon et al., 2012, Hecht et al., 2012, Wang et al., 2013, Gong et al., 2015] Social availability/Responsiveness [Horowitz and Kamvar, 2010, Sung et al., 2013] Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013] Users’ connectedness [Horowitz and Kamvar, 2010] 73 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING USERS: AARDVARK [HOROWITZ AND KAMVAR, 2010] Aardvark [Horowitz and Kamvar, 2010] • The village paradigm: towards a social dissemination of knowledge Information is passed from person to person Finding the right person rather than the right document s(ui, uj, q) = p(ui, uj) · p(ui, q) (28) = p(ui|uj) t∈T p(ui|t)(t|q) (29) 74 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING USERS: SEARCHBUDDIES [HECHT ET AL., 2012] SearchBuddies [Hecht et al., 2012] • A crowd-powered socially embedded search engine • Leveraging users’ personal network to reach the good people/information • Soshul Butterflie: Recommending people Named entity extractors (Wikipedia, openNLP, Yahoo! Placemaker) Matching with the expertise of asker’s friends (place and interests) Answers built using predefined templates Figure: c [Hecht et al., 2012] • Investigaetore: Recommending urls Filtering using a whitelist of domains Retrieving the top results Figure: c [Hecht et al., 2012] 75 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING USERS: WHOM TO MENTION [WANG ET AL., 2013] Whom to mention? [Wang et al., 2013] • Identifying potential information spreaders • Improving tweet visibility and creating social interactions • Overpassing the local network (followers) to further cascade diffusion • Learning-to-rank algorithm (Support Vector Regression): User interest (user profiling with recent tweets and score based on TF-IDF) User social tie (strength and topicality of the retweet relationship between two users) User influence (number of followers, number of received retweets/replies, and coverage of posted tweets) 76 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS THE APPROACHES: MEDIATION AT THE USER LEVEL • Identifying the right group of collaborators Expertise and interests [Nushi et al., 2015, Soulier et al., 2016, Chang and Pal, 2013] Social availability/Responsiveness [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015] Social activity [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015, Soulier et al., 2016] Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013] Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016] Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016] 77 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] CrowdSTAR: A social Task Routing Framework for Online Communities [Nushi et al., 2015] • Identifying a group of users (a crowd) • Budgeted model (number of users) modeled through a crowd skyline • Use case: peer-to-peer routing or answer provider 78 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] CrowdSTAR: A social Task Routing Framework for Online Communities [Nushi et al., 2015] • Identifying a group of users (a crowd) • Budgeted model (number of users) modeled through a crowd skyline • Use case: peer-to-peer routing or answer provider • User utility model Topic-dependent Dynamic with users’ actions (answers, posts) and time (last actions) User’s social network dependent Figure: c [Nushi et al., 2015] 78 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] • Routing questions within a crowd Trade-off between users’ utility model and ”dominating” users (crowd skyline) Pruning algorithm discarding the search space of the best user not yet included 79 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] • Routing questions within a crowd Trade-off between users’ utility model and ”dominating” users (crowd skyline) Pruning algorithm discarding the search space of the best user not yet included • Routing questions to multipe crowds Crowd summary Summary(c, t, f) = u∈skyline(c,t) f(c, t, u) |skyline(c, t)| Crowd ranking Score(c, t) = f∈F (wf · Summary(c, t, f)) 79 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016] Anwsering Twitter Question [Soulier et al., 2016] • Identifying a group of users willing to overpass the local social network • Gathering diverse pieces of information • Maximization of the group entropy Soulier, L., Tamine, L., and Nguyen, G-H. (2016). Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration, ACM CIKM 2016. 80 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016] • Learning the collaboration likelihood Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014a] 81 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016] • Learning the collaboration likelihood Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014a] • Recommending a collaborative group Identifying candidate collaborators through a temporal ranking model [Berberich and Bedathur, 2013] Extracting the collaborator group Recursive decrementation of candidate collaborators through the information gain metric Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986] 81 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion RESEARCH FIELDS AND KEY CRITICAL QUESTIONS SOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING • Social media-based collaborative information access Seeking, answering, sharing, bookmarking, and spreading information Implicit or explicit intents (sharing, questioning, and/or answering) → Improving the search outcomes through social interactions • Crowdsourcing Solving a task according to constraints (budget, time, ...) Defining, budgeting, and allocating the task → Identifying the right group of workers Emerging issue How to leverage from the wisdom of crowds? 82 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion CROWDSOURCING CONTEXT AND MOTIVATIONS • Crowdsourcing platforms Leveraging from the ”wisdom of the crowd” to perform a task [Li et al., 2014] A step forward for improving the quality of search engines for specific tasks requiring high quality data, assessments or labels [Abraham et al., 2016] Large-scale experimental evaluation reducing the cost of running and analyzing experiments [Abraham et al., 2016] Cheap, fast, reliable mechanism to gather labels [Snow et al., 2008] 83 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion CROWDSOURCING CONTEXT AND MOTIVATIONS Main issues • Optimizing the search task How to agregate answers over workers? → voting functions, stopping rules [Abraham et al., 2016] How to optimize the work between users? → number of workers [Abraham et al., 2016], task allocation [Basu Roy et al., 2015, Karger et al., 2011], group recommendation [Li et al., 2014, Rahman et al., 2015] • Evaluating the quality of answers [Oleson et al., 2011, Blanco et al., 2011, Abraham et al., 2016] 84 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion CROWDSOURCING RECOMMENDING THE RIGHT GROUP OF WORKERS The wisdom of minority [Li et al., 2014] • Leveraging from the ”minority of the crowd” to optimize the task Figure: c [Li et al., 2014] • Group discovery algorithm based on effect of features on users’ information gain Intuition Information gain metric wu: probability that user u provides the right response 1−wu L−1 : probability that user u does not provide the right response IG(u, L) = lnL + wulnwu + (1 − wu)ln 1 − wu L − 1 (30) 85 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 86 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion OPEN IDEAS OPEN IDEAS • Towards a novel probabilistic framework of relevance for CIR What is a ”good ranking” with regard to the expected synergic effect of collaboration? • Towards an axiomatic approach of relevance for CIR Are IR heuristics similar to CIR heuristics? Can relevance towards a group be modeled by a set of formally defined constraints on a retrieval function? • Dynamic IR models for CIR How to optimize long-term gains over multiple users, user-user interactions, user-system interactions and multi-search sessions? How to formalize the division of labor through the evolving of users’ information needs over time? 87 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 88 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion DISCUSSION 89 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES I Abraham, I., Alonso, O., Kandylas, V., Patel, R., Shelford, S., and Slivkins, A. (2016). How many workers to ask?: Adaptive exploration for collecting high quality labels. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, pages 473–482. Amer-Yahia, S., Benedikt, M., and Bohannon, P. (2007). Challenges in Searching Online Communities. IEEE Data Engineering Bulletin, 30(2):23–31. Amershi, S. and Morris, M. R. (2008). CoSearch: a system for co-located collaborative web search. In Proceedings of the Conference on Human Factors in Computing Systems, CHI ’08, pages 1647–1656. ACM. Balog, K., Fang, Y., de Rijke, M., Serdyukov, P., and Si, L. (2012). Expertise retrieval. Foundations and Trends in Information Retrieval, 6(2-3):127–256. Basu Roy, S., Lykourentzou, I., Thirumuruganathan, S., Amer-Yahia, S., and Das, G. (2015). Task assignment optimization in knowledge-intensive crowdsourcing. The VLDB Journal, 24(4):467–491. Berberich, K. and Bedathur, S. (2013). Temporal Diversification of Search Results. In SIGIR #TAIA workshop. ACM. Blanco, R., Halpin, H., Herzig, D. M., Mika, P., Pound, J., Thompson, H. S., and Tran, D. T. (2011). Repeatable and reliable search system evaluation using crowdsourcing. In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pages 923–932. 90 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES II Bozzon, A., Brambilla, M., and Ceri, S. (2012). Answering search queries with crowdsearcher. In Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, pages 1009–1018. Brin, S. and Page, L. (1998). The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1-7):107–117. Capra, R. (2013). Information Seeking and Sharing in Design Teams. In Proceedings of the ASIS&T Annual Meeting, ASIS&T ’13, pages 239–247. American Society for Information Science. Chang, S. and Pal, A. (2013). Routing questions for collaborative answering in community question answering. In ASONAM ’13, pages 494–501. ACM. Ehrlich, K. and Shami, N. S. (2010). Microblogging inside and outside the workplace. In Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010. Evans, B. M. and Chi, E. H. (2008). Towards a model of understanding social search. In Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, CSCW ’08, pages 485–494, New York, NY, USA. ACM. Evans, B. M. and Chi, E. H. (2010). An elaborated model of social search. Information Processing & Management (IP&M), 46(6):656–678. 91 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES III Foley, C. and Smeaton, A. F. (2009a). Evaluation of Coordination Techniques in Synchronous Collaborative Information Retrieval. CoRR, abs/0908.0. Foley, C. and Smeaton, A. F. (2009b). Synchronous Collaborative Information Retrieval: Techniques and Evaluation. In ECIR ’09, pages 42–53. Springer. Foley, C. and Smeaton, A. F. (2010). Division of Labour and Sharing of Knowledge for Synchronous Collaborative Information Retrieval. Information Processing & Management (IP&M), 46(6):762–772. Foley, C., Smeaton, A. F., and Jones., G. (2008). Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, chapter Combining. IGI Global. Foster, J. (2006). Collaborative information seeking and retrieval. Annual Review of Information Science & Technology (ARIST), 40(1):329–356. Fuchs, C. and Groh, G. (2015). Appropriateness of search engines, social networks, and directly approaching friends to satisfy information needs. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, pages 1248–1253. Fuhr, N. (2008). A probability ranking principle for interactive information retrieval. Inf. Retr., 11(3):251–265. 92 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES IV Ghosh, K. (2012). Improving e-discovery using information retrieval. In The 35th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’12, Portland, OR, USA, August 12-16, 2012, page 996. Golovchinsky, G., Qvarfordt, P., and Pickens, J. (2009). Collaborative Information Seeking. IEEE Computer, 42(3):47–51. Gong, Y., Zhang, Q., Sun, X., and Huang, X. (2015). Who will you ”@”? pages 533–542. ACM. Gonz´alez-Ib´a˜nez, R., Haseki, M., and Shah, C. (2013). Lets search together, but not too close! An analysis of communication and performance in collaborative information seeking. Information Processing & Management (IP&M), 49(5):1165–1179. Gray, B. (1989). Collaborating: finding common ground for multiparty problems. Jossey Bass Business and Management Series. Jossey-Bass. Han, S., He, D., Yue, Z., and Jiang, J. (2016). Contextual support for collaborative information retrieval. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, CHIIR ’16, pages 33–42. ACM. Hansen, P. and J¨arvelin, K. (2005). Collaborative information retrieval in an information-intensive domain. Information Processing & Management (IP&M), 41(5):1101–1119. 93 / 102
1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES V Harper, F. M., Raban, D. R., Rafaeli, S., and Konstan, J. A. (2008). Predictors of answer quality in online q&a sites. In Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI 2008, 2008, Florence, Italy, April 5-10, 2008, pages 865–874. Hecht, B., Teevan, J., Morris, M. R., and Liebling, D. J. (2012). Searchbuddies: Bringing search engines into the conversation. In WSDM ’14. Honey, C. and Herring, S. (2009). Beyond Microblogging: Conversation and Collaboration via Twitter. In HICSS, pages 1–10. Horowitz, D. and Kamvar, S. D. (2010). The Anatomy of a Large-scale Social Search Engine. In WWW ’10, pages 431–440. ACM. Imazu, M., Nakayama, S.-i., and Joho, H. (2011). Effect of Explicit Roles on Collaborative Search in Travel Planning Task. In Proceedings of the Asia Information Retrieval Societies Conference, AIRS ’11, pages 205–214. Springer. Jansen, B. J., Booth, D. L., and Spink, A. (2008). Determining the Informational, Navigational, and Transactional Intent of Web Queries. Information Processing & Management (IP&M), 44(3):1251–1266. Jeong, J.-W., Morris, M. R., Teevan, J., and Liebling, D. (2013). A crowd-powered socially embedded search engine. In ICWSM ’13. AAAI. 94 / 102
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Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics - Tutorial at ICTIR 2016

  • 1. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics Lynda Tamine Paul Sabatier University IRIT, Toulouse - France Laure Soulier Pierre and Marie Curie University LIP6, Paris - France Monday 17th October, 2016 1 / 102
  • 2. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion GOAL OF THE TUTORIAL • Introducing the notion of collaboration and the different forms of collaborative information retrieval and seeking Positioning collaborative IR within the major theoretical approaches of IR Identifying the Collaborative IR challenges • Presenting state-of-the-art theoretical models for collaborative IR Identifying the key factors affecting the design of collaborative IR models Reviewing major research progress in the area • Discussing promising research directions Bridging the gap between two (close) research branches: collaborative IR and social media IR 2 / 102
  • 3. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OUTLINE OF THE TUTORIAL • Part 1: Collaboration in information seeking and retrieval • Part 2: Models and techniques for document seeking and retrieval • Part 3: Emerging topics around collaboration • Part 4: Discussion 3 / 102
  • 4. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR What does collaboration refer to (in IR)? Collaborative information retrieval paradigms Collaborative information retrieval challenges and issues 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 4 / 102
  • 5. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE NOTION OF COLLABORATION Collaboration “A process through which parties who see different aspects of a problem can constructively explore their differences and search for solutions that go beyond their own limited vision of what is possible.” [Gray, 1989] Collaboration “Collaboration is a process in which autonomous actors interact through formal and informal negotiation, jointly creating rules and structures governing their relationships and ways to act or decide on the issues that brought them together; it is a process involving shared norms and mutually beneficial interactions.” [Thomson and Perry, 2006] Collaborative information seeking and retrieval “The study of the systems and practices that enable individuals to collaborate during the seeking, searching, and retrieval of information.” [Foster, 2006] 5 / 102
  • 6. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration • User-user (and user-system) collaboration 6 / 102
  • 7. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. • User-user (and user-system) collaboration 6 / 102
  • 8. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. • User-user (and user-system) collaboration 6 / 102
  • 9. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. • User-user (and user-system) collaboration 6 / 102
  • 10. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration 6 / 102
  • 11. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. 6 / 102
  • 12. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. Why? Ensuring long-term search gain and/or synergic effect 6 / 102
  • 13. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. Why? Ensuring long-term search gain and/or synergic effect How? Exploiting relevance feedback, using the group members’ social interactions, personal and evolving behavioral data 6 / 102
  • 14. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS • User-system collaboration What? Collaboration involves one user interacting with the system to solve an individual search goal. The collaboration is system-mediated. Why? Ensuring immediate or long-term search gains through one or multiple search sessions respectively. How? Exploiting relevance feedback, user’s personal and evolving behavioral data. Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001], dynamic IR [Jin et al., 2013, Yang et al., 2016] • User-user (and user-system) collaboration What? Collaboration involves a group of users interacting intentionally or unintentionally with each other and/or with the system to solve a shared/common search goal. The collaboration is user-mediated and/or system-mediated. Why? Ensuring long-term search gain and/or synergic effect How? Exploiting relevance feedback, using the group members’ social interactions, personal and evolving behavioral data Main IR research branches: Social media IR [Evans and Chi, 2008, Horowitz and Kamvar, 2010], collaborative filtering [Sarwar et al., 2001], collaborative IR [Shah et al., 2010, Soulier et al., 2014b] 6 / 102
  • 15. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: Static IR: system-based IR, does not learn from users eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank and Hits [Brin and Page, 1998] 7 / 102
  • 16. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: Static IR: system-based IR, does not learn from users eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank and Hits [Brin and Page, 1998] Interactive IR: exploiting feedback from users eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001] 7 / 102
  • 17. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: Static IR: system-based IR, does not learn from users eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank and Hits [Brin and Page, 1998] Interactive IR: exploiting feedback from users eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001] Dynamic IR: learning dynamically from past user-system interactions and predicts future eg. iPRP [Fuhr, 2008], interactive exploratory search [Jin et al., 2013] 7 / 102
  • 18. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: 8 / 102
  • 19. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-SYSTEM COLLABORATION • Conceptual models of IR: 8 / 102
  • 20. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION The social collaborative IR dimensions [Golovchinsky et al., 2009]: • Intent: explicit vs. implicit search goal • Depth of mediation: interface vs. algorithms (system) • Concurrency: synchronous vs. asynchronous • Location: co-located vs. remote 9 / 102
  • 21. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Main IR research branches involving user-user collaboration Collaborative IR Social media IR Collaborative filtering Intent Explicit Implicit Implicit Depth of mediation Interface/Algorithms Algorithms Algorithms Concurrency Synchronous/ Asynchronous Asynchronous Asynchronous Location Co-located/ Re- mote Remote Remote 10 / 102
  • 22. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users 11 / 102
  • 23. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users • Collaborative filtering [Resnick et al., 1994, Ma et al., 2009] Recommending search results using ratings/preferences of other users How? Inferring user’s own preferences from other users’ preferences Personalizing search results 11 / 102
  • 24. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users • Collaborative filtering [Resnick et al., 1994, Ma et al., 2009] Recommending search results using ratings/preferences of other users How? Inferring user’s own preferences from other users’ preferences Personalizing search results • Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011] Exploiting social media platforms to retrieve document/users... How? Social network analysis (graph structure, information diffusion, ...) Integrating social-based features within the document relevance scoring 11 / 102
  • 25. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION • Collaborative IR [Foster, 2006, Golovchinsky et al., 2009] Optimizing the synergic effect of co-searching How? Applying collaboration paradigms: division of labor, sharing of knowledge, awareness Supporting mediation between users • Collaborative filtering [Resnick et al., 1994, Ma et al., 2009] Recommending search results using ratings/preferences of other users How? Inferring user’s own preferences from other users’ preferences Personalizing search results • Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011] Exploiting social media platforms to retrieve document/users... How? Social network analysis (graph structure, information diffusion, ...) Integrating social-based features within the document relevance scoring Let’s have a more in-depth look on... Collaborative Information Retrieval 11 / 102
  • 26. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? USER-USER (AND USER-SYSTEM) COLLABORATION 12 / 102
  • 27. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search 13 / 102
  • 28. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor 13 / 102
  • 29. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities 13 / 102
  • 30. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities When? Synchronous vs. Asynchronous 13 / 102
  • 31. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities When? Synchronous vs. Asynchronous Where? Colocated vs. Remote 13 / 102
  • 32. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion WHAT DOES COLLABORATION REFER TO (IN IR)? THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010] What? Tasks: Complex, exploratory or fact-finding tasks, ... Application domains: Bibliographic, medical, e-Discovery, academic search Why? Shared interests Insufficient knowledge Mutual beneficial goals Division of labor Who? Groups vs. Communities When? Synchronous vs. Asynchronous Where? Colocated vs. Remote How? Crowdsourcing Implicit vs. Explicit intent User mediation System mediation 13 / 102
  • 33. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion CIR PARADIGMS [FOLEY AND SMEATON, 2010, KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010] Division of labor • Role-based division of labor • Document-based division of labor 14 / 102
  • 34. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion CIR PARADIGMS [FOLEY AND SMEATON, 2010, KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010] Division of labor • Role-based division of labor • Document-based division of labor Sharing of knowledge • Communication and shared workspace • Ranking based on relevance judgements 14 / 102
  • 35. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion CIR PARADIGMS [FOLEY AND SMEATON, 2010, KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010] Division of labor • Role-based division of labor • Document-based division of labor Sharing of knowledge • Communication and shared workspace • Ranking based on relevance judgements Awareness • Collaborators’ actions • Collaborators’ context 14 / 102
  • 36. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion THEORETICAL CHALLENGES Typical structure of a collaborative search session Challenges and issues 1 Learning from user and user-user past interactions 2 Adaptation to multi-faceted and multi-user contexts: skills, expertise, role, etc. 3 Aggregating relevant information nuggets 4 Supporting synchronous vs. asynchronous coordination 5 Modeling collaboration paradigms: division of labor, sharing of knowledge 6 Optimizing the search cost: balance in work (search) and group benefit (task outcome) 15 / 102
  • 37. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Understanding Collaborative IR Overview System-mediated CIR models User-Driven System-mediated CIR models Roadmap 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 16 / 102
  • 38. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Objectives 1 Investigating user behavior and search patterns Search processes [Shah and Gonz´alez-Ib´a˜nez, 2010, Yue et al., 2014] Search tactics and practices [Hansen and J¨arvelin, 2005, Morris, 2013, Amershi and Morris, 2008, Tao and Tombros, 2013, Capra, 2013] Role assignment [Imazu et al., 2011, Tamine and Soulier, 2015] 2 Studying the impact of collaborative search settings on performance Impact of collaboration on search performance [Shah and Gonz´alez-Ib´a˜nez, 2011, Gonz´alez-Ib´a˜nez et al., 2013] 17 / 102
  • 39. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • Study objective: Testing the feasibility of the Kuhlthau’s model of the information seeking process in a collaborative information seeking situation [Shah and Gonz´alez-Ib´a˜nez, 2010] Stage Feeling Thoughts Actions (Affective) (Cognitive) Initiation Uncertainty General/Vague Actions Selection Optimism Exploration Confusion, Frustration, Doubt Seeking relevant informa- tion Formulation Clarity Narrowed, Clearer Collection Sense of direction, Confidence Increased interest Seeking relevant or focused information Presentation Relief, Satisfaction or disap- pointment Clearer or focused 18 / 102
  • 40. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • Study objective: Testing the feasibility of the Kuhlthau’s model in collaborative information seeking situations [Shah and Gonz´alez-Ib´a˜nez, 2010] Participants: 42 dyads, students or university employees who already did a collaborative work together System: Coagmento 1 Sessions: two sessions (S1, S2) running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: simulated work tasks. eg. Task 1: Economic recession ”A leading newspaper has hired your team to create a comprehensive report on the causes and consequences of the current economic recession in the US. As a part of your contract, you are required to collect all the relevant information from any available online sources that you can find. ... Your report on this topic should address the following issues: reasons behind this recession, effects on some major areas, such as health-care, home ownership, and financial sector (stock market), unemployment statistics over a period of time, proposal execution, and effects of the economy simulation plan, and people’s opinions and reactions on economy’s downfall” 1 http://www.coagmento.org/ 19 / 102
  • 41. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • (Main) Study results: The Kuhlthau’s model stages map collaborative tasks • Initiation: number of chat messages at the stage and between stages • Selection: number of chat messages discussing the strategy • Exploration: number of search queries • Formulation: number of visited webpages • Collection: number of collected webpages • Presentation: number of moving actions for organizing collected snippets Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2010] 20 / 102
  • 42. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES • (Main) Study results: The Kuhlthau’s model stages map collaborative tasks • Initiation: number of chat messages at the stage and between stages • Selection: number of chat messages discussing the strategy • Exploration: number of search queries • Formulation: number of visited webpages • Collection: number of collected webpages • Presentation: number of moving actions for organizing collected snippets Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2010] 20 / 102
  • 43. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING SEARCH TACTICS AND PRACTICES • Study objective: Analyzing query (re)formulations and related term sources based on participants’ actions [Yue et al., 2014] Participants: 20 dyads, students who already knew each other in advance System: Collabsearch Session: one session running in running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: (T1) academic literature search, (T2) travel planning 21 / 102
  • 44. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EXPLORING SEARCH TACTICS AND PRACTICES • (Main) Study results: Individual action-based query reformulation (V, S, Q): No (significant) new findings Collaborative action-based query reformulation (SP, QP, C): Influence of communication (C) is task-dependent. Influence of collaborators’ queries (QP) is significantly higher than previous own queries (Q). Less influence of collaborators’ workspace (SP) than own workspace (S). • V: percentage of queries for which participants viewed results, one term originated from at least one page • S: percentage of queries for which participants saved results, one term originated from at least one page • Q: percentage of queries with at least one overlapping term with previous queries • SP: percentage of queries for which at least one term originated from collaborators’ workspace • QP: percentage of queries for which at least one term originated from collaborators’ previous queries • C: percentage of queries for which at least one term originated from collaborators’ communication Figure: c [Yue et al., 2014] 22 / 102
  • 45. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: STUDYING ROLE ASSIGNMENT • Study objective: Understanding differences in users’ behavior in role-oriented and non-role- oriented collaborative search sessions Participants: 75 dyads, students who already knew each other Settings: 25 dyads without roles, 50 dyads with roles (25 PM roles, 25 GS roles) System: open-source Coagmento plugin Session: one session running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: Three (3) exploratory search tasks, topics from Interactive TREC track2 Tamine, L. and Soulier, L. (2015). Understanding the impact of the role factor in collaborative information retrieval. In Proceedings of the ACM International on Conference on Information and Knowledge Management, CIKM 15, pages 4352. 2 http://trec.nist.gov/data/t8i/t8i.html 23 / 102
  • 46. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: STUDYING ROLE ASSIGNMENT • (Main) Study results Users with assigned roles significantly behave differently than users with roles Mean(s.d.) npq dt nf qn ql qo nbm W/Role GS Group 1.71(1.06) 9.99(3.37) 58.52(27.13) 65.91(31.54) 4.64(1.11) 0.44(0.18) 20(14.50) IGDiffp -0.52 -3.47*** 1.30*** 2.09*** 1.16*** 0.14*** 2.23*** PM Group 1.88(1.53) 10.47(3.11) 56.31(27.95) 56.31(27.95) 2.79(0.70) 0.39(0.08) 15(12.88) IGDiffp 0.24*** 1.45*** -2.42*** -1.69*** 0.06*** 0-0.23*** 0.05*** W/oRole Group 2.09(1.01) 13.16(3.92) 24.13(12.81) 43.58(16.28) 3.67(0.67) 0.45(0.10) 19(11.34) p-value/GS *** *** *** *** *** *** p-value/PM *** *** *** *** *** *** * W/Role vs. W/oRole ANOVA p-val. ** *** ** * 24 / 102
  • 47. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: STUDYING ROLE ASSIGNMENT • (Main) Study results Early and high level of coordination of participants without role Role drift for participants with PM role (a) GS (b) PM (c) W/oRole 25 / 102
  • 48. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE • Study objective: Evaluating the synergic effect of collaboration in information seeking [Shah and Gonz´alez-Ib´a˜nez, 2011] Participants: 70 participants, 10 as single users, 30 as dyads Settings: C1 (single users), C2 (artificial formed teams), C3 (co-located teams, different computers), C4 (co-located teams, same computer), C5 remotely located teams System: Coagmento Session: one session running in running in 7 main phases: (1) tutorial on system, (2) demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5) post-questionnaire, (6) report compilation, (7) questionnaire and interview Tasks: One exploratory search task, topic ”gulf oil spill” 26 / 102
  • 49. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE • (Main) Study results Value of remote collaboration when the task has clear independent components Remotely located teams able to leverage real interactions leading to synergic collaboration Cognitive load in a collaborative setting not significantly higher than in an individual one Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2011] 27 / 102
  • 50. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools 28 / 102
  • 51. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all 28 / 102
  • 52. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search 28 / 102
  • 53. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost 28 / 102
  • 54. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles 28 / 102
  • 55. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques 28 / 102
  • 56. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques • Back to the axiomatic relevance hypothesis (Fang et al. 2011) 28 / 102
  • 57. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques • Back to the axiomatic relevance hypothesis (Fang et al. 2011) • Role as a novel variable in the IR models ? 28 / 102
  • 58. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion EMPIRICAL UNDERSTANDING OF CIR Lessons learned • Small-group (critical mass) collaborative search is a common practice despite the lack of specific tools • The whole is greater than the sum of all • Collaborative search behavior differs from individual search behavior while some phases of theoretical models of individual search are still valid for collaborative search • Algorithmic mediation lowers the coordination cost • Roles structure the collaboration but do not guarantee performance improvement in comparison to no roles Design implications: revisit IR models and techniques • Back to the axiomatic relevance hypothesis (Fang et al. 2011) • Role as a novel variable in the IR models ? • Learning to rank from user-system, user-user interactions within multi-session search tasks? 28 / 102
  • 59. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA 29 / 102
  • 60. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA 29 / 102
  • 61. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES Collaborative IR models are based on algorithmic mediation: Systems re-use users’ search activity data to mediate the search • Data? Click-through data, queries, viewed results, result rankings, ... User-user communication • Mediation? Rooting/suggesting/enhance the queries Building personalized document rankings Automatically set-up division of labor 30 / 102
  • 62. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES Collaborative IR models are based on algorithmic mediation: Systems re-use users’ search activity data to mediate the search • Data? Click-through data, queries, viewed results, result rankings, ... User-user communication • Mediation? Rooting/suggesting/enhance the queries Building personalized document rankings Automatically set-up division of labor 30 / 102
  • 63. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES Notations Notation Description d Document q Query uj User j g Collaborative group ti term i RSV(d, q) Relevance Status Value given (d,q) N Document collection size ni Number of documents in the collection in which term ti occurs R Number of relevant documents in the collection Ruj Number of relevant documents in the collection for user uj r uj i Number of relevant documents of user uj in which term ti occurs 31 / 102
  • 64. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER GROUP-BASED MEDIATION • Enhancing collaborative search with users’ context [Morris et al., 2008, Foley and Smeaton, 2009a, Han et al., 2016] Division of labor: dividing the work by non-overlapping browsing Sharing of knowledge: exploiting personal relevance judgments, user’s authority 32 / 102
  • 65. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: GROUPIZATION, SMART SPLITTING, GROUP-HIGHLIGHTING [MORRIS ET AL., 2008] • Hypothesis setting: one or a few synchronous search query(ies) • 3 approaches Smart splitting: splitting top ranked web results using a round-robin technique, personalized-splitting of remaining results (document ranking level) Groupization: reusing individual personalization techniques towards groups (document ranking level) Hit Highlighting: highlighting user’s keywords (document browsing level) 33 / 102
  • 66. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008] Personalizing the document ranking: use the revisited BM25 weighting scheme [Teevan et al., 2005] RSV(d, q, uj) = ti∈d∩q wBM25(ti, uj) (1) wBM25(ti, uj) = log (ri + 0.5)(N − ni − Ruj + r uj i + 0.5) (ni − r uj i + 0.5)(Ruj − r uj i + 0.5 (2) N = (N + Ruj ) (3) ni = ni + r uj i (4) 34 / 102
  • 67. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008] Example Smart-splitting according to personalized scores. 35 / 102
  • 68. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B] • Hypothesis setting: multiple independent synchronous search queries • Collaborative relevance feedback: sharing collaborator’s explicit relevance judgments Aggregate the partial user relevance scores Compute the user’s authority weighting Figure: c [Foley et al., 2008, Foley and Smeaton, 2009b] 36 / 102
  • 69. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B] • A: Combining inputs of the RF process puwo(ti) = U−1 u=0 ruiwBM25(ti) (5) wBM25(ti) = log ( U−1 u=0 αu ru i Ru )(1 − U−1 u=0 αu ni − rui N − Ru ) ( U−1 u=0 αu ni − rui N − Ru )(1 − U−1 u=0 αu rui Ru ) (6) U−1 u=0 αu = 1 (7) 37 / 102
  • 70. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B] • B: Combining outputs of the RF process crwo(ti) = U−1 u=0 αuwBM25(ti, u) (8) wBM25(ti, u) = log ( ru i Ru )(1 − ni − rui N − Ru ) ( ni − rui N − Ru )(1 − rui Ru ) (9) • C: Combining outputs of the ranking process RSV(d, q) = U−1 u=0 αuRSV(d, q, u) (10) RSV(d, q, u) = ti∈d∩q wBM25(ti, u) (11) 38 / 102
  • 71. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016] • Exploit a 3-dimensional context: Individual search history HQU: queries, results, bookmarks etc.) Collaborative group HCL: collaborators’ search history (queries, results, bookmarks etc.) Collaboration HCH: collaboration behavior chat (communication) Figure: c [Han et al., 2016] 39 / 102
  • 72. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016] 1 Building a document ranking RSV(q, d) and generating Rank(d) 2 Building the document language model θd 3 Building the context language model θHx p(ti|Hx) = 1 K K k=1 p(ti|Xk) (12) p(ti|Xk) = nk Xk (13) 4 Computing the KL-divergence between θHx and θd D(θd, θHx ) = − ti p(ti|θd) log p(ti|Hx) (14) 5 Learning to rank using pairwise features (Rank(d), D(θd, θHx)) 40 / 102
  • 73. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION Enhancing collaborative search with user’s role [Pickens et al., 2008, Shah et al., 2010, Soulier et al., 2014b] • Division of labour: dividing the work based on users’ role peculiarities • Sharing of knowledge: splitting the search results 41 / 102
  • 74. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008] • Prospector/Miner as functional roles supported by algorithms: Prospector: ”..opens new fields for exploration into a data collection..”. → Draws ideas from algorithmically suggested query terms Miner: ”..ensures that rich veins of information are explored...”. → Refines the search by judging highly ranked (unseen) documents • Collaborative system architecture: Algorithmic layer: functions combining users’ search activities to produce fitted outcomes to roles (queries, document rankings). Regulator layer: captures inputs (search activities), calls the appropriate functions of the algorithmic layer, roots the outputs of the algorithmic layer to the appropriate role (user). Figure: c [Pickens et al., 2008] 42 / 102
  • 75. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008] • Prospector function: The highly-relevant terms are suggested based on: Score(ti) = Lq∈L wr(Lq)wf (Lq)rlf(ti; Lq) (15) rlf(ti; Lq): number of documents in Lq in which ti occurs. • Miner function: The unseen documents are queued according to RSV(q, d) = Lq∈L wr(Lk)wf (Lq)borda(d; Lq) (16) wr(Lq) = |seen ∈ Lq| |seen ∈ Lq| (17) wf (Lq) = |rel ∈ Lq| |seen ∈ Lq| (18) 43 / 102
  • 76. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: GATHERER AND SURVEYOR [SHAH ET AL., 2010] • Gatherer/Surveyor as functional roles supported by algorithms: Gatherer: ”..scan results of joint search activity to discover most immediately relevant documents..”. Surveyor: ”..browse a wider diversity of information to get a better understanding of the collection being searched...”. • Main functions: Merging: merging (eg. CombSum) the documents rankings of collaborators Splitting: rooting the appropriate documents according to roles (eg. k-means clustering). High precision for the Gatherer, high diversity for the Surveyor Figure: c [Shah et al., 2010] 44 / 102
  • 77. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE Domain expert/Domain novice as knowledge-based roles supported by algorithms: • Domain expert: ”..represent problems at deep structural levels and are generally interested in discovering new associations among different aspects of items, or in delineating the advances in a research focus surrounding the query topic..”. • Domain novice: ”..represent problems in terms of surface or superficial aspects and are generally interested in enhancing their learning about the general query topic..”. Soulier, L., Tamine, L., and Bahsoun, W. (2014b). On domain expertise-based roles in collaborative information retrieval. Information Processing & Management (IP&M), 50(5):752774. 45 / 102
  • 78. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] A two step algorithm: 1 Role-based document relevance scoring Pk (d|uj, q) ∝ Pk(uj|d) · Pk(d|q) (19) P(q|θd) ∝ (ti,wiq)∈q[λP(ti|θd) + (1 − λ)P(ti|θC)]wiq (20) Pk (uj|d) ∝ P(π(uj)k|θd) ∝ (ti,wk ij )∈π(uj)k [λk dj P(ti|θd) + (1 − λk dj )P(ti|θC)] wk ij (21) 46 / 102
  • 79. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] A two step algorithm: 1 Role-based document relevance scoring : parameter smoothing using evidence from novelty and specificity λk dj = Nov(d, D(uj)k) · Spec(d)β maxd ∈D Nov(d, D(uj)k) · Spec(d )β (22) with β 1 if uj is an expert −1 if uj is a novice Novelty Nov(d, D(uj) k ) = mind ∈D(uj)k d(d, d ) (23) Specificity Spec(d) = avgti∈dspec(ti) = avgti∈d( −log( fdti N ) α ) (24) 47 / 102
  • 80. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] A two step algorithm: 1 Document allocation to collaborators Classification-based on the Expectation Maximization algorithm (EM) E-step: Document probability of belonging to collaborator’s class P(Rj = 1|x k dj) = αk j · φk j (xk dj) αk j · φk j (xk dj ) + (1 − αk j ) · ψk j (xk dj ) (25) M-step : Parameter updating and likelihood estimation Document allocation to collaborators by comparison of document ranks within collaborators’ lists r k jj (d, δ k j , δ k j ) = 1 if rank(d, δk j ) < rank(d, δk j ) 0 otherwise (26) Division of labor: displaying distinct document lists between collaborators 48 / 102
  • 81. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model Let’s consider: • A collaborative search session with two users u1 (expert) and u2 (novice). • A shared information need I modeled through a query q. • A collection of 10 documents and their associated relevance score with respect to the shared information need I. t1 t2 t3 t4 q 1 0 1 0 d1 2 3 1 1 d2 0 0 5 3 d3 2 1 7 6 d4 4 1 0 0 d5 2 0 0 0 d6 3 0 0 0 d7 7 1 1 1 d8 3 3 3 3 d9 1 4 5 0 d10 0 0 4 0 Weighting vectors of documents and query: q = (0.5, 0, 0.5, 0) ; d1 = (0.29, 0.43, 0.14, 0.14) d2 = (0, 0, 0.63, 0.37) d3 = (0.12, 0.06, 0.44, 0.28) d4 = (0.8, 0.2, 0, 0) d5 = (1, 0, 0, 0) d6 = (0.3, 0, 0, 0.7) d7 = (0.7, 0.1, 0.1, 0.1) d8 = (0.25, 0.25, 0.25, 0.25) d9 = (0.1, 0.4, 0.5, 0) d10 = (0, 0, 1, 0). Users profile: π(u1)0 = π(u2)0 = q 49 / 102
  • 82. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model RSV(q, d) rank(d) Spec(d) d1 0.24 2 0.19 d2 0.02 7 0.23 d3 0.17 3 0.19 d4 0.03 6 0.15 d5 0.01 9 0.1 d6 0.02 8 0.1 d7 0.10 4 0.19 d8 0.31 1 0.19 d9 0.09 5 0.16 d10 0.01 10 0.15 • Iteration 0: Distributing top (6) documents to users: 3 most specific to the expert and the 3 less specific to the novice. Expert u1: l0 (u1, D0 ns) = {d8, d1, d3} Novice u2: l0 (u2, D0 ns) = {d7, d9, d4} 50 / 102
  • 83. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model • Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4}). Building the user’s profile. π(u1)1 = (0.5, 0, 0.5, 0) π(u2)1 = ( 0.5+0.8 2 , 0.2 2 , 0.5 2 , 0) = (0.65, 0.1, 0.25, 0). Estimating the document relevance with respect to collaborators. For user u1 : P1 (d1|u1) = P1 (d1|q) ∗ P1 (u1|d1) = 0.24 ∗ 0.22 = 0.05. P1 (d1|q) = 0.24. P1 (u1|d1) = (0.85 ∗ 2 7 + 0.15 ∗ 24 84 )0.05 + (0.85 ∗ 3 7 + 0.15 ∗ 13 84 )0 + (0.85 ∗ 1 7 + 0.15 ∗ 26 84 )0.05 + (0.85 ∗ 1 7 + 0.15 ∗ 21 84 )0 = 0.22 λ1 11 = 1∗0.19 0.23 = 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document novelty score, and 0.23 the normalization score. The normalized document scores for each collaborators are the following: P1 (d|u1) P2 (d|u2) d1 0.23 0.28 d2 0 0.03 d3 0.16 0.11 d5 0.01 0.01 d6 0.03 0.02 d7 0.12 0.14 d8 0.34 0.34 d9 0.10 0.06 d10 0.01 0.01 51 / 102
  • 84. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion SYSTEM-MEDIATED CIR MODELS ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B] Example Applying the Expert/Novice CIR model • Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4, d5}). Building the user’s profile. π(u1)1 = (0.5, 0, 0.5, 0) π(u2)1 = ( 0.5+0.8 2 , 0.2 2 , 0.5 2 , 0) = (0.65, 0.1, 0.25, 0). Estimating the document relevance with respect to collaborators. For user u1 : P1 (d1|u1) = P1 (d1|q) ∗ P1 (u1|d1) = 0.24 ∗ 0.22 = 0.05. P1 (d1|q) = 0.24 since that the user’s profile has not evolve. λ1 11 = 1∗0.19 0.23 = 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document novelty score, and 0.23 the normalization score. P1 (u1|d1) = (0.85 ∗ 2 7 + 0.15 ∗ 24 84 )0.05 + (0.85 ∗ 3 7 + 0.15 ∗ 13 84 )0 + (0.85 ∗ 1 7 + 0.15 ∗ 26 84 )0.05 + (0.85 ∗ 1 7 + 0.15 ∗ 21 84 )0 = 0.22 The normalized document scores for each collaborators are the following: P1 (d|u1) P2 (d|u2) d1 0.23 0.28 d2 0 0.03 d3 0.16 0.11 d5 0.01 0.01 d6 0.03 0.02 d7 0.12 0.14 d8 0.34 0.34 d9 0.10 0.06 d10 0.01 0.01 52 / 102
  • 85. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH Soulier, L., Shah, C., and Tamine, L. (2014a). User-driven System-mediated Collaborative Information Retrieval. In Proceedings of the Annual International SIGIR Conference on Research and Development in Information Retrieval, SIGIR 14, pages 485494. ACM. 53 / 102
  • 86. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] • Identifying users’ search behavior differences: estimating significance of differences using the Kolmogrov-Smirnov test • Characterizing users’ role 54 / 102
  • 87. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] • User’s roles modeled through patterns Intuition Number of visited documents Number of submitted queries Negative correlation Role pattern PR1,2 Search feature kernel KR1,2 Search feature-based correlation matrix FR1,2 F R1,2 =    1 if positively correlated −1 if negatively correlated 0 otherwise 55 / 102
  • 88. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] • Categorizing users’ roles Ru argmin R1,2 ||FR1,2 C (tl) u1,u2 || (27) subject to : ∀ (fj,fk)∈K R1,2 FR1,2 (fj, fk) − C (tl) u1,u2 (fj, fk)) > −1 where defined as: FR1,2 (fj, fk) C (tl) u1,u2 (fj, fk) = FR1,2 (fj, fk) − C (tl) u1,u2 (fj, fk) if FR1,2 (fj, fk) ∈ {−1; 1} 0 otherwise • Personalizing the search: [Pickens et al., 2008, Shah, 2011]. 56 / 102
  • 89. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators A collaborative search session implies two users u1 and u2 aiming at identifying information dealing with “global warming”. We present search actions of collaborators for the 5 first minutes of the session. u t actions additional information u2 0 submitted query “global warming” u1 1 submitted query “global warming” u2 8 document d1: visited comment: “interesting” u2 12 document d2: visited u2 17 document d3: visited rated: 4/5 u2 19 document d4: visited u1 30 submitted query “greenhouse effect” u1 60 submitted query “global warming definition” u1 63 document d20: visited rated: 3/5 u1 70 submitted query “global warming protection” u1 75 document d21: visited u2 100 document d5: visited rated: 5/5 u2 110 document d6: visited rated: 4/5 u2 120 document d7: visited u1 130 submitted query “gas emission” u1 132 document d22: visited rated: 4/5 u2 150 document d8: visited u2 160 document d9: visited u2 170 document d10: visited u2 200 document d11: visited comment: “great” u2 220 document d12: visited u2 240 document d13: visited u1 245 submitted query “global warming world protection” u1 250 submitted query “causes temperature changes” u1 298 submitted query “global warming world politics” 57 / 102
  • 90. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators: matching with role patterns • Role patterns Roles of reader-querier F Rread,querier = 1 −1 −1 1 , K Rread,querier = {(Nq, Np)} Role : (S (tl) u1 , S (tl) u2 , Rread,querier) → {(reader, querier), (querier, reader)} (S (tl) u1 , S (tl) u2 , Rread,querier) → (reader, querier) if S (tl) u1 (tl, Np) > S (tl) u2 (tl, Np) (querier, reader) otherwise Role of judge-querier F Rjudge,querier = 1 −1 −1 1 , K Rjudge,querier = {(Nq, Nc)} Role : (S (tl) u1 , S (tl) u2 , Rjudge,querier → {(judge, querier), (querier, judge)} (S (tl) u1 , S (tl) u2 , Rjudge,querier) → (judge, querier) if S (tl) u1 (tl, Nc) > S (tl) u2 (tl, Nc) (querier, judge) otherwise 58 / 102
  • 91. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators • Track users’ behavior each 60 seconds • F = {Nq, Nd, Nc, Nr}, respectively, number of queries, documents, comments, ratings. • Users’ search behavior S (300) u1 =      3 0 0 0 4 2 0 1 5 3 0 2 5 3 0 2 8 3 0 2      S (300) u2 =      1 4 1 1 1 7 1 3 1 10 1 3 1 13 2 3 1 13 2 3      • Collaborators’ search differences (matrix and Kolmogorov-Smirnov test) ∆ (300) u1,u2 =      2 −4 −1 −1 3 −5 −1 −2 4 −7 −1 −1 4 −10 −2 −1 7 −10 −2 −1      - Number of queries : p (tl) u1,u2 (Nq) = 0.01348 - Number of pages : p (tl) u1,u2 (Nd) = 0.01348 - Number of comments : p (tl) u1,u2 (Nc) = 0.01348 - Number of ratings : p (tl) u1,u2 (Nr) = 0.08152 59 / 102
  • 92. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion USER-DRIVEN SYSTEM-MEDIATED CIR MODELS MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A] Example Mining role of collaborators: matching with role patterns • Collaborators’ search action complementarity: correlation matrix between search differences C (300) u1,u2 =    1 −0.8186713 −0.731925 0 −0.8186713 1 0.9211324 0 −0.731925 0.9211324 1 0 0 0 0 0    • Role mining: comparing the role pattern with the sub-matrix of collaborators’ behaviors Role of reader-querier ||F Rread,querier C (300) u1,u2 || = 0 −1 − (−0.8186713) −1 − (−0.8186713) 0 = 0 0.183287 0.183287 0 The Frobenius norm is equals to: √ 0.1832872 = 0.183287. Role of judge-querier ||F Rjudge,querier C (300) u1,u2 || = 0 −1 − (−0.731925) −1 − (−0.731925) 0 = 0 0.268174 0.268174 0 The Frobenius norm is equals to: √ 0.2681742 = 0.268174. → Collaborators acts as reader/querier with u1 labeled as querier and u2 as reader (highest Np). 60 / 102
  • 93. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion OVERVIEW OF IR MODELS AND TECHNIQUES [FoleyandSmeaton,2009a] [Morrisetal.,2008]“smart-splitting” [Morrisetal.,2008]“groupization” [Pickensetal.,2008] [Shahetal.,2010] [Soulieretal.,IP&M2014b] [Soulieretal.,SIGIR2014a] Relevance collective individual Evidence source feedback interest expertise behavior role Paradigm division of labor sharing of knowledge 61 / 102
  • 94. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Research fields and key critical questions Social media-based collaborative information access Crowdsourcing 4. Open ideas 5. Discussion 62 / 102
  • 95. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? 63 / 102
  • 96. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? 63 / 102
  • 97. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? Who are the winners? How much times? 63 / 102
  • 98. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? Who are the winners? How much times? How do they win? 63 / 102
  • 99. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion COLLABORATION AND SOCIAL MEDIA-BASED IR TWO SIDES OF THE SAME COIN? • Quiz Time! What are these red points? Who are the winners? How much times? How do they win? 63 / 102
  • 100. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion RESEARCH FIELDS AND KEY CRITICAL QUESTIONS SOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING • Social media-based collaborative information access Seeking, answering, sharing, bookmarking, and spreading information Implicit or explicit intents (sharing, questioning, and/or answering) → Improving the search outcomes through social interactions • Crowdsourcing Solving a task according to constraints (budget, time, ...) Defining, budgeting, and allocating the task → Identifying the right group of workers Emerging issue How to leverage from the wisdom of crowds? 64 / 102
  • 101. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team • Social media-based collaboration Leveraging from the ”wisdom of the crowd” Tasks: social question-answering, social search, real-time search Emerging needs • Understanding the cognitive behaviors of social users sharing, assessing and disseminating information within social medias in order to achieve shared tasks leading to collective and productive outcomes. • Designing of a theoretical framework for collaborative IR within social environments 65 / 102
  • 102. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Communication during a natural disaster People sent more than 20 million Tweets about the storm between Oct 27 & Nov 1. Terms tracked: ”sandy”, ”hurricane”, #sandy, #hurricane. 66 / 102
  • 103. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS 67 / 102
  • 104. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS 67 / 102
  • 105. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] Investigating the motivation of using social media for search tasks Analyzing information needs Studying the scope of social interactions Analyzing users’ satisfaction 68 / 102
  • 106. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] Investigating the motivation of using social media for search tasks Analyzing information needs Studying the scope of social interactions Analyzing users’ satisfaction • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] 68 / 102
  • 107. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms Research questions What are the structural and semantic patterns of explicit collaboration? How groups of users with similar or complementary interests may be more likely to explicitly collaborate with each other? 1 Hurricane #Sandy (October 2012) 2 #Ebola virus epidemic (2013-2014) Lynda Tamine, Laure Soulier, Lamjed Ben Jabeur, Frdric Amblard, Chihab Hanachi, Gilles Hubert, and Camille Roth. Social media-based collaborative information access: Analysis of online crisis-related twitter conversations. ACM conference on HyperText and hypermedia, 2016. 69 / 102
  • 108. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree 70 / 102
  • 109. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree Analyzing the patterns of collaboration networks 70 / 102
  • 110. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree Analyzing the patterns of collaboration networks Extracting collaboration topics through the LDA algorithm Sandy: Insults; Prayers; Negative thoughts; Thanks Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people 70 / 102
  • 111. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016] Building the conversation tree Analyzing the patterns of collaboration networks Extracting collaboration topics through the LDA algorithm Sandy: Insults; Prayers; Negative thoughts; Thanks Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people Building the social-collaboration network over the whole graph 70 / 102
  • 112. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] 71 / 102
  • 113. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] Limitations of social media information access Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. 71 / 102
  • 114. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS CONTEXT AND MOTIVATIONS • Analyzing seekers’ behavior on social media platforms [Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015] • Main results Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] Limitations of social media information access Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. Design implications • Recommendation of collaborators (asking questions to crowd instead of followers) • Enhancement of social awareness (creating social ties to active users) 71 / 102
  • 115. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS THE APPROACHES: MEDIATION AT THE USER LEVEL • Recommending users Expertise and interests [Pal and Counts, 2011, Balog et al., 2012, Ghosh, 2012, Bozzon et al., 2012, Hecht et al., 2012, Wang et al., 2013, Gong et al., 2015, Ranganath et al., 2015] Social availability/Responsiveness [Horowitz and Kamvar, 2010, Sung et al., 2013, Ranganath et al., 2015] Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013, Ranganath et al., 2015] Users’ connectedness [Horowitz and Kamvar, 2010] • Identifying the right group of collaborators Expertise and interests [Chang and Pal, 2013, Nushi et al., 2015, Ranganath et al., 2015, Soulier et al., 2016] Social availability/Responsiveness [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015] Social activity [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015, Ranganath et al., 2015, Soulier et al., 2016] Users’ connectedness [Ranganath et al., 2015] Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013] Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016] Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016] 72 / 102
  • 116. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS THE APPROACHES: MEDIATION AT THE USER LEVEL • Recommending users Expertise and interests [Pal and Counts, 2011, Balog et al., 2012, Ghosh, 2012, Bozzon et al., 2012, Hecht et al., 2012, Wang et al., 2013, Gong et al., 2015] Social availability/Responsiveness [Horowitz and Kamvar, 2010, Sung et al., 2013] Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013] Users’ connectedness [Horowitz and Kamvar, 2010] 73 / 102
  • 117. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING USERS: AARDVARK [HOROWITZ AND KAMVAR, 2010] Aardvark [Horowitz and Kamvar, 2010] • The village paradigm: towards a social dissemination of knowledge Information is passed from person to person Finding the right person rather than the right document s(ui, uj, q) = p(ui, uj) · p(ui, q) (28) = p(ui|uj) t∈T p(ui|t)(t|q) (29) 74 / 102
  • 118. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING USERS: SEARCHBUDDIES [HECHT ET AL., 2012] SearchBuddies [Hecht et al., 2012] • A crowd-powered socially embedded search engine • Leveraging users’ personal network to reach the good people/information • Soshul Butterflie: Recommending people Named entity extractors (Wikipedia, openNLP, Yahoo! Placemaker) Matching with the expertise of asker’s friends (place and interests) Answers built using predefined templates Figure: c [Hecht et al., 2012] • Investigaetore: Recommending urls Filtering using a whitelist of domains Retrieving the top results Figure: c [Hecht et al., 2012] 75 / 102
  • 119. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING USERS: WHOM TO MENTION [WANG ET AL., 2013] Whom to mention? [Wang et al., 2013] • Identifying potential information spreaders • Improving tweet visibility and creating social interactions • Overpassing the local network (followers) to further cascade diffusion • Learning-to-rank algorithm (Support Vector Regression): User interest (user profiling with recent tweets and score based on TF-IDF) User social tie (strength and topicality of the retweet relationship between two users) User influence (number of followers, number of received retweets/replies, and coverage of posted tweets) 76 / 102
  • 120. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS THE APPROACHES: MEDIATION AT THE USER LEVEL • Identifying the right group of collaborators Expertise and interests [Nushi et al., 2015, Soulier et al., 2016, Chang and Pal, 2013] Social availability/Responsiveness [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015] Social activity [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015, Soulier et al., 2016] Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013] Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016] Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016] 77 / 102
  • 121. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] CrowdSTAR: A social Task Routing Framework for Online Communities [Nushi et al., 2015] • Identifying a group of users (a crowd) • Budgeted model (number of users) modeled through a crowd skyline • Use case: peer-to-peer routing or answer provider 78 / 102
  • 122. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] CrowdSTAR: A social Task Routing Framework for Online Communities [Nushi et al., 2015] • Identifying a group of users (a crowd) • Budgeted model (number of users) modeled through a crowd skyline • Use case: peer-to-peer routing or answer provider • User utility model Topic-dependent Dynamic with users’ actions (answers, posts) and time (last actions) User’s social network dependent Figure: c [Nushi et al., 2015] 78 / 102
  • 123. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] • Routing questions within a crowd Trade-off between users’ utility model and ”dominating” users (crowd skyline) Pruning algorithm discarding the search space of the best user not yet included 79 / 102
  • 124. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015] • Routing questions within a crowd Trade-off between users’ utility model and ”dominating” users (crowd skyline) Pruning algorithm discarding the search space of the best user not yet included • Routing questions to multipe crowds Crowd summary Summary(c, t, f) = u∈skyline(c,t) f(c, t, u) |skyline(c, t)| Crowd ranking Score(c, t) = f∈F (wf · Summary(c, t, f)) 79 / 102
  • 125. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016] Anwsering Twitter Question [Soulier et al., 2016] • Identifying a group of users willing to overpass the local social network • Gathering diverse pieces of information • Maximization of the group entropy Soulier, L., Tamine, L., and Nguyen, G-H. (2016). Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration, ACM CIKM 2016. 80 / 102
  • 126. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016] • Learning the collaboration likelihood Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014a] 81 / 102
  • 127. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016] • Learning the collaboration likelihood Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014a] • Recommending a collaborative group Identifying candidate collaborators through a temporal ranking model [Berberich and Bedathur, 2013] Extracting the collaborator group Recursive decrementation of candidate collaborators through the information gain metric Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986] 81 / 102
  • 128. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion RESEARCH FIELDS AND KEY CRITICAL QUESTIONS SOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING • Social media-based collaborative information access Seeking, answering, sharing, bookmarking, and spreading information Implicit or explicit intents (sharing, questioning, and/or answering) → Improving the search outcomes through social interactions • Crowdsourcing Solving a task according to constraints (budget, time, ...) Defining, budgeting, and allocating the task → Identifying the right group of workers Emerging issue How to leverage from the wisdom of crowds? 82 / 102
  • 129. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion CROWDSOURCING CONTEXT AND MOTIVATIONS • Crowdsourcing platforms Leveraging from the ”wisdom of the crowd” to perform a task [Li et al., 2014] A step forward for improving the quality of search engines for specific tasks requiring high quality data, assessments or labels [Abraham et al., 2016] Large-scale experimental evaluation reducing the cost of running and analyzing experiments [Abraham et al., 2016] Cheap, fast, reliable mechanism to gather labels [Snow et al., 2008] 83 / 102
  • 130. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion CROWDSOURCING CONTEXT AND MOTIVATIONS Main issues • Optimizing the search task How to agregate answers over workers? → voting functions, stopping rules [Abraham et al., 2016] How to optimize the work between users? → number of workers [Abraham et al., 2016], task allocation [Basu Roy et al., 2015, Karger et al., 2011], group recommendation [Li et al., 2014, Rahman et al., 2015] • Evaluating the quality of answers [Oleson et al., 2011, Blanco et al., 2011, Abraham et al., 2016] 84 / 102
  • 131. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion CROWDSOURCING RECOMMENDING THE RIGHT GROUP OF WORKERS The wisdom of minority [Li et al., 2014] • Leveraging from the ”minority of the crowd” to optimize the task Figure: c [Li et al., 2014] • Group discovery algorithm based on effect of features on users’ information gain Intuition Information gain metric wu: probability that user u provides the right response 1−wu L−1 : probability that user u does not provide the right response IG(u, L) = lnL + wulnwu + (1 − wu)ln 1 − wu L − 1 (30) 85 / 102
  • 132. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 86 / 102
  • 133. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion OPEN IDEAS OPEN IDEAS • Towards a novel probabilistic framework of relevance for CIR What is a ”good ranking” with regard to the expected synergic effect of collaboration? • Towards an axiomatic approach of relevance for CIR Are IR heuristics similar to CIR heuristics? Can relevance towards a group be modeled by a set of formally defined constraints on a retrieval function? • Dynamic IR models for CIR How to optimize long-term gains over multiple users, user-user interactions, user-system interactions and multi-search sessions? How to formalize the division of labor through the evolving of users’ information needs over time? 87 / 102
  • 134. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion PLAN 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion 88 / 102
  • 135. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion DISCUSSION 89 / 102
  • 136. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES I Abraham, I., Alonso, O., Kandylas, V., Patel, R., Shelford, S., and Slivkins, A. (2016). How many workers to ask?: Adaptive exploration for collecting high quality labels. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, pages 473–482. Amer-Yahia, S., Benedikt, M., and Bohannon, P. (2007). Challenges in Searching Online Communities. IEEE Data Engineering Bulletin, 30(2):23–31. Amershi, S. and Morris, M. R. (2008). CoSearch: a system for co-located collaborative web search. In Proceedings of the Conference on Human Factors in Computing Systems, CHI ’08, pages 1647–1656. ACM. Balog, K., Fang, Y., de Rijke, M., Serdyukov, P., and Si, L. (2012). Expertise retrieval. Foundations and Trends in Information Retrieval, 6(2-3):127–256. Basu Roy, S., Lykourentzou, I., Thirumuruganathan, S., Amer-Yahia, S., and Das, G. (2015). Task assignment optimization in knowledge-intensive crowdsourcing. The VLDB Journal, 24(4):467–491. Berberich, K. and Bedathur, S. (2013). Temporal Diversification of Search Results. In SIGIR #TAIA workshop. ACM. Blanco, R., Halpin, H., Herzig, D. M., Mika, P., Pound, J., Thompson, H. S., and Tran, D. T. (2011). Repeatable and reliable search system evaluation using crowdsourcing. In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pages 923–932. 90 / 102
  • 137. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES II Bozzon, A., Brambilla, M., and Ceri, S. (2012). Answering search queries with crowdsearcher. In Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, pages 1009–1018. Brin, S. and Page, L. (1998). The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1-7):107–117. Capra, R. (2013). Information Seeking and Sharing in Design Teams. In Proceedings of the ASIS&T Annual Meeting, ASIS&T ’13, pages 239–247. American Society for Information Science. Chang, S. and Pal, A. (2013). Routing questions for collaborative answering in community question answering. In ASONAM ’13, pages 494–501. ACM. Ehrlich, K. and Shami, N. S. (2010). Microblogging inside and outside the workplace. In Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010. Evans, B. M. and Chi, E. H. (2008). Towards a model of understanding social search. In Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, CSCW ’08, pages 485–494, New York, NY, USA. ACM. Evans, B. M. and Chi, E. H. (2010). An elaborated model of social search. Information Processing & Management (IP&M), 46(6):656–678. 91 / 102
  • 138. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES III Foley, C. and Smeaton, A. F. (2009a). Evaluation of Coordination Techniques in Synchronous Collaborative Information Retrieval. CoRR, abs/0908.0. Foley, C. and Smeaton, A. F. (2009b). Synchronous Collaborative Information Retrieval: Techniques and Evaluation. In ECIR ’09, pages 42–53. Springer. Foley, C. and Smeaton, A. F. (2010). Division of Labour and Sharing of Knowledge for Synchronous Collaborative Information Retrieval. Information Processing & Management (IP&M), 46(6):762–772. Foley, C., Smeaton, A. F., and Jones., G. (2008). Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, chapter Combining. IGI Global. Foster, J. (2006). Collaborative information seeking and retrieval. Annual Review of Information Science & Technology (ARIST), 40(1):329–356. Fuchs, C. and Groh, G. (2015). Appropriateness of search engines, social networks, and directly approaching friends to satisfy information needs. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, pages 1248–1253. Fuhr, N. (2008). A probability ranking principle for interactive information retrieval. Inf. Retr., 11(3):251–265. 92 / 102
  • 139. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES IV Ghosh, K. (2012). Improving e-discovery using information retrieval. In The 35th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’12, Portland, OR, USA, August 12-16, 2012, page 996. Golovchinsky, G., Qvarfordt, P., and Pickens, J. (2009). Collaborative Information Seeking. IEEE Computer, 42(3):47–51. Gong, Y., Zhang, Q., Sun, X., and Huang, X. (2015). Who will you ”@”? pages 533–542. ACM. Gonz´alez-Ib´a˜nez, R., Haseki, M., and Shah, C. (2013). Lets search together, but not too close! An analysis of communication and performance in collaborative information seeking. Information Processing & Management (IP&M), 49(5):1165–1179. Gray, B. (1989). Collaborating: finding common ground for multiparty problems. Jossey Bass Business and Management Series. Jossey-Bass. Han, S., He, D., Yue, Z., and Jiang, J. (2016). Contextual support for collaborative information retrieval. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, CHIIR ’16, pages 33–42. ACM. Hansen, P. and J¨arvelin, K. (2005). Collaborative information retrieval in an information-intensive domain. Information Processing & Management (IP&M), 41(5):1101–1119. 93 / 102
  • 140. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion REFERENCES V Harper, F. M., Raban, D. R., Rafaeli, S., and Konstan, J. A. (2008). Predictors of answer quality in online q&a sites. In Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI 2008, 2008, Florence, Italy, April 5-10, 2008, pages 865–874. Hecht, B., Teevan, J., Morris, M. R., and Liebling, D. J. (2012). Searchbuddies: Bringing search engines into the conversation. In WSDM ’14. Honey, C. and Herring, S. (2009). Beyond Microblogging: Conversation and Collaboration via Twitter. In HICSS, pages 1–10. Horowitz, D. and Kamvar, S. D. (2010). The Anatomy of a Large-scale Social Search Engine. In WWW ’10, pages 431–440. ACM. Imazu, M., Nakayama, S.-i., and Joho, H. (2011). Effect of Explicit Roles on Collaborative Search in Travel Planning Task. In Proceedings of the Asia Information Retrieval Societies Conference, AIRS ’11, pages 205–214. Springer. Jansen, B. J., Booth, D. L., and Spink, A. (2008). Determining the Informational, Navigational, and Transactional Intent of Web Queries. Information Processing & Management (IP&M), 44(3):1251–1266. Jeong, J.-W., Morris, M. R., Teevan, J., and Liebling, D. (2013). A crowd-powered socially embedded search engine. In ICWSM ’13. AAAI. 94 / 102