Dynamic Context-Sensitive PageRank for Expertise Mining<br />2nd Int. Conf. on Social Informatics (SocInfo'10)<br />27-29 ...
Presentation Outline<br />Overview<br />Motivation<br />Human-Provided Services (HPS) Crowdsourcing Example<br />Human Int...
3<br />Overview<br />Paradigm: human and service interactions<br />Open dynamic ecosystems<br />People and software servic...
Motivation: Human computation/SOA<br />4<br />BPEL4People/WS-HT<br /><ul><li>User driven versus modeledtasksin workflow</l...
No collaboration link between humans</li></ul>Process flow<br />Web services<br />People activity/human task<br />task1<br...
Reputation mechanism and expertise ranking in large-scale systems</li></ul>Knowledge sharing <br />platform <br />
5<br />Human Provided Service: Crowdsourcing Example<br />Discovery<br />HPS Interactions<br />Definition<br />Schall et a...
Overview Metrics<br />6<br /><ul><li>Classification of Metrics</li></ul>Schall (2009), Human Interactions in Mixed Systems...
Challenges<br />7<br /><ul><li>How to find the most relevant expert?
How to calculate the expertise of people in an automated manner?
How to account for changing interests and the skill level in different fields of interest?</li></ul>My Approach<br /><ul><...
Interaction mining using link-intensity weights
Personalization based on interaction context
Aggregated importance using query terms</li></li></ul><li>8<br />Discovery and Ranking<br />Expert Seeker (e.g., Crowdsour...
(2) Create interaction graph (offline)
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Dynamic Context-Sensitive PageRankfor Expertise Mining

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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.

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Dynamic Context-Sensitive PageRankfor Expertise Mining

  1. 1. Dynamic Context-Sensitive PageRank for Expertise Mining<br />2nd Int. Conf. on Social Informatics (SocInfo'10)<br />27-29 October, 2010, Austria<br />Daniel Schall<br />schall@infosys.tuwien.ac.at<br />Vienna University of Technology<br />http://www.infosys.tuwien.ac.at/staff/dschall/<br />29. Oct. 2010<br />
  2. 2. Presentation Outline<br />Overview<br />Motivation<br />Human-Provided Services (HPS) Crowdsourcing Example<br />Human Interaction Metrics<br />Dynamic Skill and Activity-based PageRank (DSARank)<br />Experiments and Conclusion<br />2<br />
  3. 3. 3<br />Overview<br />Paradigm: human and service interactions<br />Open dynamic ecosystems<br />People and software services integrated into evolving “solutions“<br />Communications and coordination<br />„Anytime-anywhere“ pervasive infrastructures and mobility<br />Mass collaboration<br />Knowledge sharing and social interaction<br />Crowdsourcing<br />Human computation on the Web<br />… software service<br />… user<br />… human/service interaction<br />
  4. 4. Motivation: Human computation/SOA<br />4<br />BPEL4People/WS-HT<br /><ul><li>User driven versus modeledtasksin workflow</li></ul>Crowdsourcing<br /><ul><li>Human Intelligent Tasks (e.g., Amazon Mechanical Turk)
  5. 5. No collaboration link between humans</li></ul>Process flow<br />Web services<br />People activity/human task<br />task1<br />task2<br />Tasks<br />task3<br />Requester<br />task4<br /><ul><li>Modeling of human interactions in dynamic service-oriented systems
  6. 6. Reputation mechanism and expertise ranking in large-scale systems</li></ul>Knowledge sharing <br />platform <br />
  7. 7. 5<br />Human Provided Service: Crowdsourcing Example<br />Discovery<br />HPS Interactions<br />Definition<br />Schall et al. (2008), Unifying Human and Software Services in Web-Scale Collaborations, IEEE Computer<br />
  8. 8. Overview Metrics<br />6<br /><ul><li>Classification of Metrics</li></ul>Schall (2009), Human Interactions in Mixed Systems - Architecture, Protocols, and Algorithms (PhD Thesis)<br />
  9. 9. Challenges<br />7<br /><ul><li>How to find the most relevant expert?
  10. 10. How to calculate the expertise of people in an automated manner?
  11. 11. How to account for changing interests and the skill level in different fields of interest?</li></ul>My Approach<br /><ul><li>Dynamic Skill and Activity-based PageRank
  12. 12. Interaction mining using link-intensity weights
  13. 13. Personalization based on interaction context
  14. 14. Aggregated importance using query terms</li></li></ul><li>8<br />Discovery and Ranking<br />Expert Seeker (e.g., Crowdsourcing engine)<br /><ul><li>(1) Logging interactions
  15. 15. (2) Create interaction graph (offline)
  16. 16. (3) Aggregate ranking results based on preferences (online)</li></ul>Schall (2009), Human Interactions in Mixed Systems - Architecture, Protocols, and Algorithms<br />
  17. 17. Ranking Algorithm: Random surfer model<br />9<br />… node<br />Web Graph<br />… surfer<br />… Web link<br />1/2<br />1/3<br />With a certain probability, I will jump (“teleport”) to a random Web page.<br />Page et al. (1999), The PageRank Citation Ranking: Bringing Order to the Web.<br />
  18. 18. Ranking Algorithm: Behavior model<br />10<br />… document<br />Interaction Graph<br />… user<br />6<br />5<br />… link<br />3<br />w1,3<br />1<br />4<br />w1,2<br />w2,4<br />2<br />I will contact User 2 depending on the link weight w1,2. The link weight is based on strength and intensities of interactions.<br />I will contact some other user. For example, to start a new collaboration by relaying a message. <br />
  19. 19. Ranking Algorithm: Interaction context<br />11<br /><ul><li>Users interact in different contexts with different intensities</li></ul>context 1 (e.g., topic = WS Addressing)<br />context 2 (e.g., topic = WS Policy)<br />2<br />1<br />1<br />Interaction intensity context 1<br />Interaction intensity context 2<br /><ul><li>Personalize ranking (i.e., expertise) for different contexts</li></li></ul><li>Context-dependent DSARank<br /><ul><li>(1) Identify context of interactions („tags“)
  20. 20. (2) Select relevant links and people
  21. 21. (3) Create weighted subgraph (for context)
  22. 22. (4) Perform mining</li></ul>Context 1<br />3<br />1<br />w1,3<br />4<br />w1,2<br />w2,4<br />2<br />4<br />User 1’s expertise in context 1<br />1<br />w1,4<br />User 1’s expertise in context 2<br />w1,3<br />3<br />Context 2<br />Calculated offline<br />E.g., p(u) = w1 IIL(u) + w2 availability(u)<br />Combined online based on preferences<br />12<br />
  23. 23. Results<br />13<br /><ul><li>Real dataset (Email)
  24. 24. High interaction intensity reveals key people
  25. 25. Best informed users</li></ul>(see paper for detailed experiment results)<br />
  26. 26. Conclusion<br />14<br /><ul><li>Crowdsourcing gains popularity
  27. 27. Amazon Mechanical Turk
  28. 28. Recognition from scientific community
  29. 29. Human-Provided Services
  30. 30. Supporting versatile crowdsourcing scenarios
  31. 31. Context-sensitive expertise
  32. 32. Important in collaborative crowd environments
  33. 33. Based on topic sensitive interaction mining</li></li></ul><li>Thanks for your attention!<br />http://en.wikipedia.org/wiki/The_Turk<br />Daniel Schall<br />schall@infosys.tuwien.ac.at<br />Vienna University of Technology<br />http://www.infosys.tuwien.ac.at/staff/dschall/<br />

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