Recommender Systems with Social Regularization Hao Ma, Dengyong Zhou, Chao Liu Microsoft Research Michael R. Lyu The Chinese University of Hong Kong Irwin King AT&T Labs Research The Chinese University of Hong Kong
Recommender Systems are Everywhere 2 Web 2.0 Web Sites are Everywhere 3
Trust-aware Recommender Systems These Methods utilize the inferred implicit or observed explicit trust information to further improve traditional recommender systems. [J. ODonovan and B. Smyth, IUI 2005] [P. Massa and P. Avesani, RecSys 2007] [H. Ma, I. King, and M. R. Lyu, SIGIR 2009] Based on the motivation that I trust you => I
have similar tastes with you. 4 Comparison Trust-aware Social-based Trust network: unilateral
relations Social friend network: mutual relations Trust relations can be treated as similar relations
Friends are very diverse, and may have different tastes Few datasets available on the Web Lots of Web sites have social network implementation
5 Contents of This Work Focusing on social-based recommendation problems Two methods are proposed based on matrix factorization with social regularization terms Can be applied to trust-aware recommender systems. Experiments on two large datasets
Douban (social friend network) Epinions (trust network) 6 Problem Definition Social Network Information
User-Item Rating Matrix 7 Low-Rank Matrix Factorization for Collaborative Filtering Objective function Ui ,Vj : low dimension column vectors to represent user/item preferences. 8
Social Regularization I Average-based regularization Minimize Uis taste with the average tastes of Uis friends. The similarity function Sim(i, f) allows the social regularization term to treat users friends differently. 9
Social Regularization I Gradients 10 Social Regularization II Individual-based regularization This approach allows similarity of friends tastes to be individually
considered. It also indirectly models the propagation of tastes. 11 Social Regularization II Gradients 12 Similarity Function
Vector Space Similarity (VSS) or Cosine Similarity Pearson Correlation Coefficient (PCC) 13 Dataset I Douban
Chinese Web 2.0 Web site with social friend network service The largest online book, movie and music review and rating site in China We crawled 129,940 users and 58,541 movies with 16,830,839 movie ratings The total number of friend links between users is 1,692,952 14
Dataset II Epinions A well-known English general consumer review and rating site Every member maintains a trust list which presents a user network of trust relationships We crawled 51,670 users who have rated a total of 83,509 different items. The total number of ratings is
631,064 The total number of issued trust statements is 511,799 15 Metrics MAE and RMSE 16
Performance Comparison 17 Impact of Parameter beta 18
Impact of Similarity Functions 19 Conclusions We proposed two new social recommendation methods Our approaches perform better than other traditional and trust-aware recommendation
methods The methods scale well since the employed algorithm is linear with the observation of ratings 20 Future Work Employ more accurate similarity functions Consider item side regularization
Apply similar techniques to other social applications, like social search problems 21 Thanks! Q&A
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