Online tools for collaboration and social platforms have become omnipresent in Web-based environments. Interests and skills of people evolve over time depending in performed activities and joint collaborations. We believe that ranking models for recommending experts or collaboration partners should not only rely on profiles or skill information that need to be manually maintained and updated by the user. In this work we address the problem of expertise mining based on performed interactions between people. We argue that an expertise mining algorithm must consider a person's interest and activity level in a certain collaboration context. Our approach is based on the PageRank algorithm enhanced by techniques to incorporate contextual link information. An approach comprising two steps is presented. First, offline analysis of human interactions considering tagged interaction links and second composition of ranking scores based on preferences. We evaluate our approach using an email interaction network.

1. Dynamic Context-Sensitive PageRank for Expertise Mining 2nd Int. Conf. on Social Informatics (SocInfo'10) 27-29 October, 2010, Austria Daniel Schall schall@infosys.tuwien.ac.at Vienna University of Technology http://www.infosys.tuwien.ac.at/staff/dschall/ 29. Oct. 2010

2. Presentation Outline Overview Motivation Human-Provided Services (HPS) Crowdsourcing Example Human Interaction Metrics Dynamic Skill and Activity-based PageRank (DSARank) Experiments and Conclusion 2

3. 3 Overview Paradigm: human and service interactions Open dynamic ecosystems People and software services integrated into evolving “solutions“ Communications and coordination „Anytime-anywhere“ pervasive infrastructures and mobility Mass collaboration Knowledge sharing and social interaction Crowdsourcing Human computation on the Web … software service … user … human/service interaction

6. Reputation mechanism and expertise ranking in large-scale systemsKnowledge sharing platform

7. 5 Human Provided Service: Crowdsourcing Example Discovery HPS Interactions Definition Schall et al. (2008), Unifying Human and Software Services in Web-Scale Collaborations, IEEE Computer

10. How to calculate the expertise of people in an automated manner?

12. Interaction mining using link-intensity weights

13. Personalization based on interaction context

15. (2) Create interaction graph (offline)

16. (3) Aggregate ranking results based on preferences (online)Schall (2009), Human Interactions in Mixed Systems - Architecture, Protocols, and Algorithms

17. Ranking Algorithm: Random surfer model 9 … node Web Graph … surfer … Web link 1/2 1/3 With a certain probability, I will jump (“teleport”) to a random Web page. Page et al. (1999), The PageRank Citation Ranking: Bringing Order to the Web.

18. Ranking Algorithm: Behavior model 10 … document Interaction Graph … user 6 5 … link 3 w1,3 1 4 w1,2 w2,4 2 I will contact User 2 depending on the link weight w1,2. The link weight is based on strength and intensities of interactions. I will contact some other user. For example, to start a new collaboration by relaying a message.

20. (2) Select relevant links and people

21. (3) Create weighted subgraph (for context)

22. (4) Perform miningContext 1 3 1 w1,3 4 w1,2 w2,4 2 4 User 1’s expertise in context 1 1 w1,4 User 1’s expertise in context 2 w1,3 3 Context 2 Calculated offline E.g., p(u) = w1 IIL(u) + w2 availability(u) Combined online based on preferences 12

24. High interaction intensity reveals key people

25. Best informed users(see paper for detailed experiment results)

27. Amazon Mechanical Turk

28. Recognition from scientific community

29. Human-Provided Services

30. Supporting versatile crowdsourcing scenarios

31. Context-sensitive expertise

32. Important in collaborative crowd environments