social recommendation
TRANSCRIPT
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
Algorithms
User/Item KNN; Clustering-basedGraph-based Algorithms Matrix FactorizationInformation DiffusionProbabilistic Model…
User-Item (Rating)
Context:
Social Relations
Social Tagging
Time Location Query
Information item
Merchandise/Ads
People
Output:Input:
Community
Effectiveness of Social Relationship
• CF vs SF• Social filtering approach outperforms the
CF approach in all variants of the experiment
Familiarity vs Similarity• Extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations• Trustworthy
G. Groh et.al, Recommendations in Taste Related Domains: Collaborative Filtering vs. Social Filtering, GROUP07
I.Guy, et.al Personalized Recommendation of Social Software ItemsBased on Social Relations, ACM Recsys09
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
Fusing via weighted-similarity friendship only
Ia Ib IcUa 1 0 1
Ub 0 1 0
Uc 1 1 0
Ua Ub Uc
Ua 1 0 1
Ub 0 1 0
Uc 1 0 1
User
Item User
User
User-Item Matrix Friendship Matrix
Simui Simfri
Simui+fri (ua ,ub ) = λ *Simui (ua ,ub ) + (1-λ)*Simfri (ua ,ub )
Optimal λ was learned by cross-validation
Neighborhood Similarity Formula:
Konstas, et, al. On social networks and collaborative recommendation, SIGIR09Yuan, et, al. Augmenting Collaborative Recommender by Fusing Explicit Social Relationships. ACM RecSys09, workshop of Social Recommender
Fusing via weighted-similarity membership only
Ia Ib IcUa 1 0 1
Ub 0 1 0
Uc 1 1 0
Ga Gb Gc
Ua 0 0 1
Ub 0 1 1
Uc 1 0 0
User
Item Group
User
User-Item Matrix Membership Matrix
Simui Simmem
Simui+mem (ua ,ub ) = λ *Simui (ua ,ub ) + (1-λ)*Simmem (ua ,ub )Neighborhood Similarity Formula:
Fusing via weighted-similarity friendship + membership
Ia Ib IcUa 1 0 1
Ub 0 1 0
Uc 1 1 0
Ga Gb Gc
Ua 0 0 1
Ub 0 1 1
Uc 1 0 0
User
Item Group
User
User-Item Matrix Membership Matrix
Simui Simmem
Simui+fri+mem (ua ,ub ) = λSimui + (1-λ)[β Simmem + (1- β)Simfri ]
Optimal λand β was learned by cross-validation
Neighborhood Similarity Formula:
Ua Ub Uc
Ua 1 0 1
Ub 0 1 0
Uc 1 0 1
User
User
Friendship Matrix
Simfri
Experimental results cont.
• The baseline is user-based CF on user-item matrix only by cosine similarity
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
Model 1: Classic user-item bipartite graph with attributes
u3u2u1
i1 i2 i3
age
category
locgender
color price
item
user
attributes
attributes
Model 2: user-item bipartite graph with social relationships
membership
Ga UaIa
Gb
Gc
Ub
Uc
Ib
Ic
user’s behavior on itemfriendship
U
I
G
user node
item node
group node
u3
u2
u1 i1
i2
i3
itemuser
friendship
Model 3: Triple models & Temporal models
user item
tag
user item
group
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
Fig.1 How adoption patterns affect the recommendations
Fig.2 illustration of Info. Flow
user item
session
User-Item-Session
Session: a combinational node of user & item
X. Song et.al, Personalized Recommendation Driven by Information Flow, SIGIR 06
Model 5
TrustWalker: RW on a trust network
A heterogeneous social network:
User-Resource-Tag-Category
Zhang & Tang, Recommendation over a Heterogeneous Social Network, WAIM08
M Jamali, TrustWalker: a random walk model for combining trust-based and item-based recommendation, SIGKDD09
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
1
1
1
Shadow vertex
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~!