Social Recommendation

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  • 1.Social Recommendation Yuan Quan ( )IBM Research - China

2. About me Yuan Quan M.S. Computer Science and Engineering, Xian Jiaotong University, 2003-2006. B.S. Computer Science and Engineering, Xian 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, 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 peoples 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 OverviewInput: Output:Information item User-Item (Rating) Algorithms Merchandise/Ads User/Item KNN; Clustering-based Social RelationsGraph-based AlgorithmsPeople Matrix FactorizationSocial Tagging Information Diffusion Community Probabilistic ModelContext:TimeLocationQuery 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 studyincluding 90 users, indicates superiority of the familiarity network as CF approach in all variants of thea 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 onlyItem UserIaIbIc UaUbUcUa1 0 1Ua1 0 1 User UserUb0 1 0Ub0 1 0Uc1 1 0Uc1 0 1 User-Item Matrix Friendship Matrix Simui SimfriNeighborhood Similarity Formula:Simui+fri(ua,ub) = *Simui(ua,ub) + (1-)*Simfri (ua,ub) Optimal was learned by cross-validationKonstas, et, al. On social networks and collaborative recommendation, SIGIR09Yuan, et, al. Augmenting Collaborative Recommender by Fusing Explicit Social Relationships. ACMRecSys09, workshop of Social Recommender 15. Fusing via weighted-similarity membership only ItemGroupIaIb Ic Ga GbGcUa 1 01Ua00 1 User UserUb 0 10Ub01 1Uc 1 10Uc10 0User-Item MatrixMembership Matrix Simui SimmemNeighborhood Similarity Formula:Simui+mem(ua,ub) = *Simui(ua,ub) + (1-)*Simmem(ua,ub) 16. Fusing via weighted-similarity friendship + membershipItemUser Group Ia Ib IcUaUbUc Ga GbGcUa10 1Ua101Ua00 1 UserUser UserUb01 0Ub010Ub01 1Uc11 0Uc101Uc10 0 User-Item MatrixFriendship MatrixMembership MatrixSimuiSimfri SimmemNeighborhoodSimilarity Formula:Simui+fri+mem(ua,ub) = Simui + (1-)[ Simmem + (1- )Simfri ] Optimal and 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 graphwith attributesattributes agegenderlocitem i1 i2i3useru1 u2 u3attributes category colorprice 20. Model 2: user-item bipartite graph with social relationshipsuser itemGaIaUai1u1GbIb Ub friendshipu2i2 Gc Ic Uc u3 i3 Uuser node membership friendshipIitem node users behavior on item Ggroup node 21. Model 3: Triple models & Temporal modelstag group user item user itemUser-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 commonSession: a combinationalnode 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 5TrustWalker: RW on a trust network M Jamali, TrustWalker: a random walk model for combining A heterogeneous social network: trust-based and item-based recommendation, SIGKDD09User-Resource-Tag-Category Zhang & Tang, Recommendation over aHeterogeneous 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 walksOne dimension RWTwo dimension RW 26. Random Walk cont. RW on graph: PageRank is a random walk on graph RWs 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 - adsorptionShadowvertex 111 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~!