Social Recommendation

Social Recommendation Yuan Quan (袁泉) IBM Research - China

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

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

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)

Collaborative Filtering - Amazon

Friends’ Recommendation – Facebook

Social Recommendation based on massive people’s wisdom

Recommending Friends via Social Network

Music Recommendation based on Taste & Friendship/Membership

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

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

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

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

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)

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

Experimental results cont. • The baseline is user-based CF on user-item matrix only by cosine similarity

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

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

Model 3: Triple models & Temporal models tag group user item user item User-Item-Tag User-Item-Group

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

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

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

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

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

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

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.

Thanks~!

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Social Recommendation

by gu wendong on Apr 06, 2010

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