Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Evaluating Tag-Based Information Access in Image CollectionsChristoph Trattner
The availability of social tags has greatly enhanced access to information.
Tag clouds have emerged as a new “social” way to find
and visualize information, providing both one-click access to information
and a snapshot of the “aboutness” of a tagged collection.
A range of research projects explored and compared different tag
artifacts for information access ranging from regular tag clouds to
tag hierarchies. At the same time, there is a lack of user studies that
compare the effectiveness of different types of tag-based browsing
interfaces from the users point of view. This paper contributes to
the research on tag-based information access by presenting a controlled
user study that compared three types of tag-based interfaces
on two recognized types of search tasks – lookup and exploratory
search. Our results demonstrate that tag-based browsing interfaces
significantly outperform traditional search interfaces in both performance
and user satisfaction. At the same time, the differences
between the two types of tag-based browsing interfaces explored in
our study are not as clear.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Evaluating Tag-Based Information Access in Image CollectionsChristoph Trattner
The availability of social tags has greatly enhanced access to information.
Tag clouds have emerged as a new “social” way to find
and visualize information, providing both one-click access to information
and a snapshot of the “aboutness” of a tagged collection.
A range of research projects explored and compared different tag
artifacts for information access ranging from regular tag clouds to
tag hierarchies. At the same time, there is a lack of user studies that
compare the effectiveness of different types of tag-based browsing
interfaces from the users point of view. This paper contributes to
the research on tag-based information access by presenting a controlled
user study that compared three types of tag-based interfaces
on two recognized types of search tasks – lookup and exploratory
search. Our results demonstrate that tag-based browsing interfaces
significantly outperform traditional search interfaces in both performance
and user satisfaction. At the same time, the differences
between the two types of tag-based browsing interfaces explored in
our study are not as clear.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Massive Open Online Courses, kurz MOOCs, sind gerade das Thema. Tausende von Teilnehmern melden sich online für Kurse an, greifen auf Lernressourcen zu und vernetzen sich miteinander. Doch MOOCs sind nur ein Stichwort in der Diskussion um die Zukunft des Lernens. Der Bildungsexperte Dr. Jochen Robes (Weiterbildungsblog) stellt Ihnen in diesem Foliensatz diesen und weitere Trends im cloud-basierten Lernen vor.
Anschließend werden entlang der vorgestellten Trends Erfahrungen aus dem SAP Umfeld - incl. neuster Produkte- erläutert.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Massive Open Online Courses, kurz MOOCs, sind gerade das Thema. Tausende von Teilnehmern melden sich online für Kurse an, greifen auf Lernressourcen zu und vernetzen sich miteinander. Doch MOOCs sind nur ein Stichwort in der Diskussion um die Zukunft des Lernens. Der Bildungsexperte Dr. Jochen Robes (Weiterbildungsblog) stellt Ihnen in diesem Foliensatz diesen und weitere Trends im cloud-basierten Lernen vor.
Anschließend werden entlang der vorgestellten Trends Erfahrungen aus dem SAP Umfeld - incl. neuster Produkte- erläutert.
Social media potentiale nutzen vortrag von value advice am 26.11.2012 in sc...Value Advice
Social Media in der Anwendung für kleine bis mittlere Unternehmen. Heranführung an das Thema Social Media. Grundgedanken der Umsetzung und Checklisten um diese zu erarbeiten.
Schaffert, Sandra; Bürger, Tobias; Hilzensauer, Wolf; Schneider, Cornelia & Wieden-Bischof, Diana (2010). Empfehlungen im Web. Konzepte und Realisierungen. Band 3 der Reihe „Social Media“ (hrsg. von Georg Güntner und Sebastian Schaffert), Salzburg: Salzburg Research. (ISBN 978-3-902448-16-3) - frei zugängliche Version (CC Lizenz!) - auch im Buchhandel erhältlich!
Vortrag Generation Online - Dr. Katja BettKatja Bett
Generation Online: Medienkompetenz und das Social Web - eine Vortrag von Dr. Katja Bett Arbeitskreis Schule Wirtschaft November 2012 im RWE Bildungszentrum in Wanderath. Inhalte und Fragestellungen im Vortrag: (1) Was machen die Jugendlichen eigentlich im Social Web? (2) Was lernen sie und was lernen sie nicht? (3) Wie können wir den Erwerb von Medienkompetenz in Schule und Betriebe fördern? (4) Wie müssen wir als Schule/Betrieb auf die medienbestimmte Welt der Jugendlichen reagieren?
Immer mehr Personen sind Teil sozialer Netzwerke im Internet, tauschen Meinungen aus und berichten über ihre Erfahrungen mit Produkten und Marken. Für Unternehmen wird das Thema Social Media damit immer wichtiger, da sie selbst an der Konversation oftmals weder aktiv noch passiv teilnehmen. Wer an einer Konversation nicht teilnimmt, hat auch keinen Einfluss auf deren Inhalte.
»Man kann nicht nicht kommunizieren!« (Paul Watzlawick)
Welche Konsequenzen darin liegen, an diesen Gesprächen im sozialen Netz nicht teilzunehmen wird im Kurs genauer thematisiert. Die frühzeitige Teilnahme an sozialen Netzwerken ist unabdingbar.
Wir bieten einen ersten Einblick in die Funktionsweisen und klären über die Potenziale und Risiken bei der strategischen Nutzung des sozialen Netzes auf.
[DE]
Diese Präsentation enthält Teaser-Folien zum Seminar "Intranet Strategie und Governance" der Kongress Media Akademie. Durchführungsorte und -daten unter: http://www.akademie.kongressmedia.de/
[EN] This is a teaser presentation about a seminar on Intranet strategy and governancen. MOst slides are in German language.
Am 6. Juni fand der 14. Bildungskongress der Know How! AG statt. Es war eine abwechslungsreiche Veranstaltung mit den beiden Leitthemen Enterprise 2.0 und Best Practice.
Veit Mathauer hielt einen Vortrag zum Thema „2.0: Planung ermöglicht Spontaneität“ und beantwortete Fragen wie: Was gehört zu einer erfolgreichen externen und internen Kommunikation? Was sind die Vorteile interner Blogs und „Wikis“ und wie schafft man es, seine Mitarbeiter dazu zu motivieren sich dauerhaft an diesen Plattformen zu beteiligen? Am Beispiel von Sympra erklärte Veit Mathauer wie das funktionieren kann.
Ihr Weg zum erfolgreichen Social Intranet!
Erfahrene Interne Kommunikations- und Social Intranet-Experten arbeiten in den Workshops zwei Tage mit den Teilnehmern an ihren Top-Themen. Keynotes von Referenten angesehener Unternehmen eröffnen die beiden Workshop-Tage.
In einem Live-Check stellen wir Ihnen Social-Intranet-Lösungen anhand ausgewählter Anwendungsfälle vor und zeigen Ihnen, was die einzelnen Tools können. Die Praxistage bieten außerdem die Möglichkeit, sich mit Kollegen fachlich auszutauschen und zu vernetzen.
Präsentation vom Castlecamp 2008 - Social Media im Tourismus: Grundlagen zu Web 2.0 und Social Web, Ambient Awareness, Weakties und peripheral Vision Konzept, Märkte sind Gespräche, Was ist ein Social Media Marketer, welche Motivationen treiben Nutzer in das Social Web? etc.
SMRC Hamburg - Planung eines Social Media Auftritts für Employer Branding & R...Henner Knabenreich
Social Media ja, aber bitte nicht überstürzt!
Planung eines Social Media Auftritts für Employer Branding & Recruiting
Vortrag anlässlich der SMRC Hamburg.