eTrust: Understanding Trust Evolution in an Online World

1. eTrust: Understanding Trust Evolution in an Online World August 12-16, 2012 KDD2012

2. Trust and Its Evolution • Trust plays an important role in helping online users collect reliable information • Abundant research on static trust for making good decisions and finding high quality content • However, trust evolves as people interact and time passes by • It is necessary to study its evolution • Its study can advance online trust research for trust related applications

3. Our Contributions • We identify the differences of trust study in physical and online worlds • We investigate how to study online trust evolution • We show if this study can help improve the performance of trust related applications

4. Research in Physical and Online Worlds • Trust evolution in a physical world - Step 1: inviting a group of participants ( a small group) - Step 2: recording their sociometric information - Step 3: recording conditions or situations for the change • Differences encountered in an online world - Users are world-widely distributed - Sociometric information on trust is unavailable - Passive observation is the modus operandi to gather data

5. Studying Online Trust Evolution • Overcoming the challenge of passive observation • Where can we find relevant data for trust study (an issue about environment) • How can we infer about the information about trust (an issue about methodology) • Modeling online trust evolution • How to incorporate social theories mathematically • Evaluating the gain of trust evolution study • Rating prediction and trust prediction

6. Online Rating System time t

7. Online Rating System time t time t+1

8. Online Rating System Temporal Information time t time t+1

9. Social Science theories • Correlations between rating and user preference - Dynamics of rating • Correlations between user preference and trust - Drifting user preferences

10. Methodology for Trust Evolution Social theories Social theories Dynamics of user preference Trust Evolution Temporal information, rating etc Rating Prediction Online Rating System

11. Our Framework: eTrust

12. Components of eTrust Part 1 Part 2 Part 4 Part 3

13. Part 1: Modeling Rating via User Preference • Rating is related to user preference and item characteristic - - is the preference of i-th user in time t, is the characteristic of j-thitem and K is the number of latent facets of items

14. Part 2: Modeling Rating via Trust Network • People is likely to be influenced by their trust networks Trust strength between i-th and v-th users in the k-th facet Decaying the earlier rating

15. Part 3: Modeling Trust and User preference • Modeling the correlation between trust and user preference is preference similarity vector in the k-th facet and is a user specific bias

16. Part 4: Modeling Change of User Preference • Modeling the change of user preference cis a function to control how user preference change, λ controls the speed of change

17. Experiments • Datasets • Findings from the study of trust evolution • Can eTrust help improve trust related applications? - Rating Prediction - Trust Prediction

18. Experiments • Datasets • Findings from the study of trust evolution • Can eTrust help improve trust related applications? - Rating Prediction - Trust Prediction

19. Datasets • Epinions • Product review sites • Statistics

20. http://www.public.asu.edu/~jtang20/datasetcode/truststudy.htmhttp://www.public.asu.edu/~jtang20/datasetcode/truststudy.htm

21. Splitting the Dataset • Epinions is separated into 11 timestamps 11thJan, 2010, 11thJan, 2001, 11thJan, 2009, 11thJan, 2002, T2 ……. T1 T11 T10

22. Experiments • Datasets • Findings from the study of trust evolution • Can eTrust help improve trust related applications? - Rating Prediction - Trust Prediction

23. Speed of Change of Trust • The evolution speed of an open triad is 6.12 times of that of a closed triad

24. User preferences drift over time

25. The speed of change varies with people and facets

26. Experiments • Datasets • Findings from the study of trust evolution • Can eTrust help improve trust related applications? - Rating Prediction - Trust Prediction

27. Experiments • Datasets • Findings from the study of trust evolution • Can eTrust help improve trust related applications? - Rating Prediction - Trust Prediction

28. Applications of eTrust: Rating Prediction • Given ratings before T, we predict ratings in T+1 as,

29. Testing Datasets • We further divide data in T11into two testing datasets - N: the ratings involved in new items or new users(10.06%) - K: the remaining ratings

30. Comparison of Rating Prediction

31. Experiments • Datasets • Findings from the study of trust evolution • Can eTrust help improve trust related applications? - Rating Prediction - Trust Prediction

32. Applications of eTrust: Trust Prediction • The likelihood of trust establishing is estimated as,

33. Testing Datasets • We also divide data in T11into two testing datasets - E: trust relations established among existing users - N: trust relations involved in new users (23.51%)

34. Comparison of Trust Prediction

35. Future Work • Seek more applications for eTrust - Ranking evolution - Recommendation systems - Helpfulness prediction • Generalize eTrust to other online worlds - e-commerce

36. Questions Acknowledgments: This work is, in part, sponsored by ARO via a grant (#025071). Comments and suggestions from DMML members and reviewers are greatly appreciated.