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

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

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

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

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

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

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

P3: Trust Bias Bob -> Elva (T BE )? T BE = = Overall avg. rating: Trustor bias: Trustee bias: Alice BobCarol David Elva

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

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

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

Modeling Multi-Aspect 12

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

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

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

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

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

Computing Propagation Propagation: (z ij ) 18

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

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

Experiments Datasets  Advogato (  PGP (Pretty Good Privacy) Effectiveness: how accurate is the proposed MATRI for trust inference? Efficiency: how fast is the proposed MATRI? 21

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

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

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

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

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

Q&A Thanks ！ 27