1. Yuan Yao Joint work with Hanghang Tong, Xifeng Yan, Feng Xu, and Jian Lu MATRI: A Multi-Aspect and Transitive Trust Inference Model 1 May 13-17, WWW 2013

2. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 2

3. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 3

4. Trust “Trust is the subjective probability by which an individual (trustor), expects that another individual (trustee) will perform well on a given action.” 4

5. Trust Inference How to infer the unknown trust relationships? E.g., to what extent should Bob trust Elva? 5 Bob Carol Alice Elva David : Trust Trust Properties: Transitivity, Multi-Aspect, Trust Bias

6. P1: Trust Transitivity 6 Trust transitivity (or trust propagation): T BE = T BA * T AE Bob Carol Alice Elva David Bob -> Elva (T BE )? T AE T BA

7. P2: Multi-Aspect 7 Bob -> Elva (T BE )? Trustor Preferences Trustee Capabilities

8. P3: Trust Bias Bob -> Elva (T BE )? T BE = 0.4 - 0.2 + 0.5 = 0.7 8 Overall avg. rating: 0.5 0.20.4-0.3-0.10.1 Trustor bias: Trustee bias: -0.10.20.10.2-0.2 Alice BobCarol David Elva

9. This Paper Q1: how to characterize multi-aspect trust directly from trust ratings? Q2: how to incorporate trust bias? Q3: how to incorporate trust transitivity? 9

10. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 10

11. Modeling Multi-Aspect 11 item rating -> user -> item -->

12. Modeling Multi-Aspect 12

13. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 13

14. Incorporating Trust Bias Three types of trust bias:  Global bias (μ), trustor bias (x), trustee bias (y) 14

15. Computing Bias Global Bias: 15 Trustor Bias: Trustee Bias:

16. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 16

17. Incorporating Trust Transitivity Four types of trust propagation 17 (a) T * T (b) T ’ (c) T ’ * T (d) T * T ’ : known trust : inferred trust (i,j) Z ij

18. Computing Propagation Propagation: (z ij ) 18

19. Our Final Model: MaTrI Multi-Aspect Trust bias Trust transitivity 19

20. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 20

21. Experiments Datasets  Advogato (http://www.trustlet.org/wiki/Advogato_dataset)  PGP (Pretty Good Privacy) Effectiveness: how accurate is the proposed MATRI for trust inference? Efficiency: how fast is the proposed MATRI? 21

22. Hang et al., Operators for propagation trust and their evaluation in social networks. AAMAS 2009. Massa et al., Controversial users demand local trust metrics. AAAI 2005. Wang et al., Trust representation and aggregation in a distributed agent system. AAAI 2006. Guha et al., Propagation of trust and distrust. WWW 2004. Effectiveness Results Comparisons with trust propagation models. (better) 22 Our method

23. Effectiveness Results HCD: C. Hsieh et al., Low rank modeling of signed networks. KDD 2012. KBV: Y. Koren et al., Matrix factorization techniques for recommender systems. Computer 2009 23 Comparisons with related methods. Smaller is better. Our method

24. Efficiency Results 24 Pre-computational time: O(m+n) Online response time: O(1) Our method

25. Roadmap Background and Motivations Modeling Multi-Aspect Incorporating Trust Bias Incorporating Trust Transitivity Empirical Evaluations Conclusions 25

26. Conclusions An Integral Trust-Inference Model  Q1: how to characterize multi-aspect?  A1: analogy to recommendation problem  Q2: how to incorporate trust bias?  A2: treat bias as specified factors  Q3: how to incorporate trust transitivity?  A3: propagation through factorization Empirical Evaluations  Effectiveness: >10% improvement  Efficiency:  linear in pre-computation  constant online response 26

27. Q&A Thanks ！ 27