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Data Mining and Machine Learning Lab eTrust: Understanding Trust Evolution in an Online World Jiliang Tang, Huiji Gao and Huan Liu Computer Science and.

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Presentation on theme: "Data Mining and Machine Learning Lab eTrust: Understanding Trust Evolution in an Online World Jiliang Tang, Huiji Gao and Huan Liu Computer Science and."— Presentation transcript:

1 Data Mining and Machine Learning Lab eTrust: Understanding Trust Evolution in an Online World Jiliang Tang, Huiji Gao and Huan Liu Computer Science and Engineering Arizona State University Atish Das Sarma eBay Research Lab eBay Inc. 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 1.We identify the differences of trust study in physical and online worlds 2.We investigate how to study online trust evolution 3.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 time t time t+1 Temporal Information

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

11 Our Framework: eTrust

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

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-th item 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 c is 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 /truststudy.htm

21 Splitting the Dataset Epinions is separated into 11 timestamps 11 th Jan, 2001, 11 th Jan, 2010, ……. 11 th Jan, 2009, 11 th Jan, 2002, T2T2 T1T1 T 10 T 11

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 T 11 into 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 T 11 into 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.


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