Download pdf - 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





• 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







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







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



• Concept-level Overview• Effectiveness of social relationship• Technologies on fusing social relationships




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~!