Social Recommendation

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  • 1. Social Recommendation Yuan Quan (袁 泉) IBM Research - China
  • 2. About me • Yuan Quan – M.S. Computer Science and Engineering, Xi’an Jiaotong University, 2003-2006. – B.S. Computer Science and Engineering, Xi’an Jiaotong University, 1999-2003. • 2006 ~ now IBM China Research Lab • Research interest – Personalized recommendation – User modeling – Social network analysis
  • 3. Agenda • Social Recommendation – Categories & samples – Definition • Concept-level Overview • Effectiveness of Social Relationship • Technologies on Social Fusion – Pair-wise similarity fusion – Graph-based fusion • Graph-based data models • Algorithms
  • 4. Social Recommendation Categories • Collaborative Filtering is a kind of social recommender – compare with traditional content-based approach • Recommendation from friends – Offline: daily recommendation from friends – Online: news feeds from friends on Facebook, Re-tweet, 开心转帖 • Any recommendation using social data as input – Social relationship / social network • friendship, membership, trust/distrust, follow – Social tagging & bookmarking • Recommendation over Social Media (Blog, YouTube)
  • 5. Collaborative Filtering - Amazon
  • 6. Friends’ Recommendation – Facebook
  • 7. Social Recommendation based on massive people’s wisdom
  • 8. Recommending Friends via Social Network
  • 9. Music Recommendation based on Taste & Friendship/Membership
  • 10. Agenda • Social Recommendation – Categories & samples – Definition • Concept-level Overview • Effectiveness of Social Relationship • Technologies on Social Fusion – Pair-wise similarity fusion – Graph-based fusion • 5 graph-based data models • Algorithms – Random walk – Class label propagation - adsorption
  • 11. Social Recommendation Overview Input: Output: Information item User-Item (Rating) Algorithms Merchandise/Ads User/Item KNN; Clustering-based Social Relations Graph-based Algorithms People Matrix Factorization Social Tagging Information Diffusion Community Probabilistic Model… Context: Time Location Query
  • 12. Effectiveness of Social Relationship • CF vs SF Familiarity vs Similarity • Social filtering approach outperforms the • Extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as CF approach in all variants of the a basis for recommendations experiment • Trustworthy G. Groh et.al, Recommendations in Taste Related Domains: I.Guy, et.al Personalized Recommendation of Social Software Items Collaborative Filtering vs. Social Filtering, GROUP07 Based on Social Relations, ACM Recsys09
  • 13. Agenda • Social Recommendation – Categories & samples – Definition • Concept-level Overview • Effectiveness of Social Relationship • Technologies on Social Fusion – Pair-wise similarity fusion – Graph-based fusion • 5 graph-based data models • Algorithms – Random walk – Class label propagation - adsorption
  • 14. Fusing via weighted-similarity friendship only Item User Ia Ib Ic Ua Ub Uc Ua 1 0 1 Ua 1 0 1 User User Ub 0 1 0 Ub 0 1 0 Uc 1 1 0 Uc 1 0 1 User-Item Matrix Friendship Matrix Simui Simfri Neighborhood Similarity Formula: Simui+fri(ua,ub) = λ *Simui(ua,ub) + (1-λ)*Simfri (ua,ub) Optimal λ was learned by cross-validation Konstas, et, al. On social networks and collaborative recommendation, SIGIR09 Yuan, et, al. Augmenting Collaborative Recommender by Fusing Explicit Social Relationships. ACM RecSys09, workshop of Social Recommender
  • 15. Fusing via weighted-similarity membership only Item Group Ia Ib Ic Ga Gb Gc Ua 1 0 1 Ua 0 0 1 User User Ub 0 1 0 Ub 0 1 1 Uc 1 1 0 Uc 1 0 0 User-Item Matrix Membership Matrix Simui Simmem Neighborhood Similarity Formula: Simui+mem(ua,ub) = λ *Simui(ua,ub) + (1-λ)*Simmem(ua,ub)
  • 16. Fusing via weighted-similarity friendship + membership Item User Group Ia Ib Ic Ua Ub Uc Ga Gb Gc Ua 1 0 1 Ua 1 0 1 Ua 0 0 1 User User User Ub 0 1 0 Ub 0 1 0 Ub 0 1 1 Uc 1 1 0 Uc 1 0 1 Uc 1 0 0 User-Item Matrix Friendship Matrix Membership Matrix Simui Simfri Simmem Neighborhood Similarity Formula: Simui+fri+mem(ua,ub) = λSimui + (1-λ)[β Simmem + (1- β)Simfri ] Optimal λand β was learned by cross-validation
  • 17. Experimental results cont. • The baseline is user-based CF on user-item matrix only by cosine similarity
  • 18. Agenda • Social Recommendation – Categories & samples – Definition • Concept-level Overview • Effectiveness of Social Relationship • Technologies on Social Fusion – Pair-wise similarity fusion – Graph-based fusion • 5 graph-based data models • Algorithms – Random walk – Class label propagation - adsorption
  • 19. Model 1: Classic user-item bipartite graph with attributes attributes age gender loc item i1 i2 i3 user u1 u2 u3 attributes category color price
  • 20. Model 2: user-item bipartite graph with social relationships user item Ga Ia Ua i1 u1 Gb Ib Ub friendship u2 i2 Gc Ic Uc u3 i3 U user node membership friendship I item node user’s behavior on item G group node
  • 21. Model 3: Triple models & Temporal models tag group user item user item User-Item-Tag User-Item-Group
  • 22. Model 4: Temporal Models • Information flow – u and r have 40 items in common – u and v have 40 items in common Session: a combinational node of user & item session Fig.1 How adoption patterns affect the recommendations user item User-Item-Session Fig.2 illustration of Info. Flow X. Song et.al, Personalized Recommendation Driven by Information Flow, SIGIR 06
  • 23. Model 5 TrustWalker: RW on a trust network M Jamali, TrustWalker: a random walk model for combining A heterogeneous social network: trust-based and item-based recommendation, SIGKDD09 User-Resource-Tag-Category Zhang & Tang, Recommendation over a Heterogeneous Social Network, WAIM08
  • 24. Agenda • Social Recommendation – Categories & samples – Definition • Concept-level Overview • Effectiveness of social relationship • Technologies on fusing social relationships – Pair-wise similarity fusion – Graph-based fusion • 5 graph-based data models • Algorithms – Random walk – Class label propagation - adsorption
  • 25. Random Walk • Random walk is a mathematical formalization of a trajectory that consists of taking successive random steps. Often, random walks are assumed to have Markov properties: • E.g. the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging animal, the financial status of a gambler can all be modeled as random walks One dimension RW Two dimension RW
  • 26. Random Walk cont. • RW on graph: PageRank is a random walk on graph • RW’s usage in recommendation – For each user, rank & recommend top-N unknown items – Calculate similarities between nodes • E.g. user-user nodes similarity for neighborhood • Similarity measures: Average Commute-Time, Average FPT, L+, etc. • Notice: – Transition probability matrix – Personalized vector – Damping factor
  • 27. Class propagation - adsorption Shadow vertex 1 1 1 Baluja, et.al, Video Suggestion and Discovery for YouTube: Taking Random Walks Through the View Graph, WWW08
  • 28. Our work • Augmenting Collaborative Recommender by Fusing Explicit Social Relationships. – First work to discover membership as useful as friendship in recommendation. • ACM RecSys09, workshop of Social Recommender • Model Users’ Long-/short-term Preference on Graph for Recommendation. – First work to balance the influence of long-/short-term preference on graph • Submitted to SIGKDD10. • Temporal Dynamic of Social Trust for Recommendation – First work to study the temporal dynamics of social relations and its usage for recommendation • Draft for ACM Recsys10.
  • 29. Thanks~!