Circle-based Recommendation in Online Social Networks

Transcription

1 Circle-based Recommendation in Online Social Networks Xiwang Yang, Harald Steck*, and Yong Liu Polytechnic Institute of NYU * Bell Labs/Netflix 1

2 Outline q Background & Motivation q Circle-based RS Trust Circle Inference Trust Value Assignment Model Training q Evaluation q Conclusion 2

3 Background q Information overloading in web 2.0 recommender system widely deployed 3 q Battling data sparsity social networks as input social homophily, social influence q Social networks include multiple circles more refined information user trusts different subsets of friends in different domains(cars, Music ) construct social circles specific to item categories

4 Collaborative Filtering(CF) q Most Used and Well Known Approach for Recommendation q Finds Users with Similar Interests to the target User q Aggregating their opinions to make a recommendation. 4

5 User Based Collaborative Filtering Target Customer Aggregator u w u u r w u u, i 5 Prediction

6 6 Item-based Collaborative Filtering

7 Item-Item Collaborative Filtering Aggregator i i w r i w i u, i 7 Prediction

8 Matrix Factorization(BaseMF) [NIPS08] q Model based approach q Latent feature of user u, q Latent feature of item i, P i q Prediction Model: ˆ T R = r + Q P PR ( PQ,, σ ) = [ NR ( Rˆ ), σ ] 2 2 R u, i u, i R all u all i T 2 I [ NR ( ui, rm QP u i ), σ R] all u all i = + ui, m u i q Objective Function 1 ˆ 2 λ 2 2 ( Rui, Rui, ) + ( P F + Q F) 2 2 ( ui, ) obs. R ui, Q u I 1 d 1 d R ui, P and Q have normal priors 8

9 9 Social Recommenders Everywhere

10 Related Work-Social Recommender q Social Recommendation (SoRec) Model CIKM 08 Factorizing social trust matrix together with user rating matrix q Social Trust Ensemble (STE) Model SIGIR 09 User s rating influenced by social friends q SocialMF Model RecSys 10 User s latent feature(taste) influenced by social friends Handle trust propagation in social network q Using whole trust network for item rating prediction 10

11 Proposed Improvements for Current Social Recommender q Social networks include multiple circles A more refined social trust information richer information Want to incorporate circle information into Social Recommender Ideally, use trust circles specific to an item category when predict rating in this category E.g. Trust Circle of Music, Trust Circle of Cars, etc 11

12 Proposed Improvements for Current Social Recommender Existing circles(google+, facebook) not corresponding to an item category 12

13 Proposed Improvements for Current Social Recommender q In existing multi-category rating datasets, no circle information q User trusts different subsets of friends in different domains(cars, Music ) q User trusts different friends differently, related to friend s expertise value q Should use trust circle specific to item category 13

14 Outline q Background & Motivation q Circle-based RS Trust Circle Inference Trust Value Assignment Model Training q Evaluation q Conclusion & Future work 14

15 Trust Circle Inference q User v is in inferred circle c of u iff u trust v in original social network and both of them have rating in category c Original Social Network Inferred circle for category C1 Inferred circle for category C2 Inferred circle for category C3 15

16 Trust Value Assignment q CircleCon1: Equal Trust each user in the inferred circle gets assigned the same trust value S = const if v C ( c)* ( c) uv, u v C ( c ) u S ( c)* uv, = 1 S, = 1 C, v C ( c)* ( c) ( c) uv u u 16

17 Trust Value Assignment q CircleCon2: Expertise-based Trust assign a higher trust value or weight to the friends that are experts in the circle / category. 17 Variant a: Expertise value of user u proportional to u s number of ratings in a circle Variant b: Expertise based on u s number of rating in circle and voting value from u s followers in this circle

18 CircleCon3: Trust Splitting Original trust link trust link in c1 trust link in c2 q Most trust due to followee s rating in one category q Likelihood u2 trusts u1 in C1, C2? q Infer likelihood proportional on u2 s number of ratings in C1 and C2. q Assign trust value in a category proportional to the likelihood u2 trusts u1 in a category 18

19 CircleCon3: Trust Splitting N c u c = 9, N = u 1 1 S = 0.9, S = 0.1 ( c1) ( c2) u, u u, u q Normalize across followees S = S S ( c)* ( c) ( c) uv, uv, uv, v C ( c ) u 19

20 Outline q Background & Motivation q Circle-based RS Trust Circle Inference Trust Value Assignment Model Training q Evaluation q Conclusion & Future work 20

21 Model Training q Training with ratings from each category Predict user s rating in category c Input rating: rating in category c Input social network: Circle c ˆ ( c ) ( c ) ( c ) ( c ) T ui, = m + u i R r Q P ( c) ( c) ( c) ( c) ( c)* L ( R, Q, P, S ) = 1 ( R Rˆ ) 2 ( ui, ) obs. ( c) ( c) 2 ui, ui, β + ( Q S Q )( Q S Q ) 2 λ ( c) 2 ( c) 2 + ( P F + Q F) 2 ( c) ( c)* ( c) ( c) ( c)* ( c) T u u, v v u u, v v all u v v Solved by gradient descent Ø β is social information weight ( c) ( c) 0 ( c) 0 Ø P, Q ( c) Ø i is the number of 0 items in category c i d u d 21

22 Model Training q Training with ratings for all categories Predict user s rating in category c Input rating: rating from all categories Input social network: Circle c ( c) ( c) ( c) ( c)* L ( R, Q, P, S ) = 1 ( R ˆ ui, Rui, ) 2 P ( ui, ) obs., Q ( c) i d ( c) u d 2 β + ( Q S Q )( Q S Q ) 2 λ ( c) 2 ( c) 2 + ( P F + Q F) 2 ( c) ( c)* ( c) ( c) ( c)* ( c) T u u, v v u u, v v all u v v

23 Outline q Background & Motivation q Circle-based RS Trust Circle Inference Trust Value Assignment Model Training q Evaluation q Conclusion & Future work 23

24 24 Epinions Data

25 Performance Metrics RMSE = MAE = ( ui, ) R ( ui, ) R test test ( R Rˆ ) R test ui, ui, R Rˆ R test ui, ui, 2 25

26 26 Training with per-category ratings

27 Training with per-category ratings ( c) ( c) ( c) ( c) ( c)* L ( R, Q, P, S ) = 1 ( c) ˆ ( c) 2 λ ( c) 2 ( c) 2 ( Rui, Rui, ) + ( P F + Q F) 2 2 ( u, i) obs. β + ( Q S Q )( Q S Q ) 2 ( c) ( c)* ( c) ( c) ( c)* ( c) T u u, v v u u, v v all u v v 27

28 Training with ratings from all categories 28 CircleCon3 of training with per-category rating

29 Conclusions q Propose a novel Circle-based Social Recommendation framework Split original social network to different circles, one circle corresponding to one item category User trusts different subsets of friends in different domains(cars, Music ) User trusts different friends differently, based on friend s expertise q Outperforms the state-of-the-art social collaborative filtering algorithms q Show the promising future of circle-construction techniques in Social Recommender 29

30 Thanks! Q & A 30

31 Trust Value Assignment q CircleCon1: Equal Trust 31

32 CircleCon2: Expertise-based Trust q Variant a: Expertise based on number of rating in a circle 32

33 CircleCon2: Expertise-based Trust q Variant b: Expertise value based on user s number of rating in circle and voting value from followers in this circle E = N φ ( c) ( c) ( c) v v v Dw records the proportions of ratings user w assigned in all categories. It reflects the interest distribution of w cross all categories 33

34 34 Training with ratings from all categories

35 35 Training with ratings from all categories