Circle-based Recommedation in Online Social Networks

Circle-based Recommendation in Online Social Networks

Xiwang Yang, Harald Steck*, and Yong Liu

Polytechnic Institute of NYU* Bell Labs/Netflix

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Outline Background & Motivation

Circle-based RS Trust Circle Inference Trust Value Assignment Model Training

EvaluationConclusion & Future work

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Social Recommenders Everywhere

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Collaborative Filtering(CF)

Most Used and well-known Approach for Recommendation Finds Users with Similar Interests to the target user

Matrix Factorization(BaseMF)’

Latent features for users Latent features for items Prediction Model

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P and Q have normal priors

0u dQ

0i dP

,ˆ Tu i m u iR r Q P

Related Work-Social Recommender

Social Recommendation (SoRec) Model CIKM’08 Factorizing social trust matrix together with user rating

matrix

Social Trust Ensemble (STE) Model SIGIR’09 User’s rating influenced by social friends

SocialMF Model RecSys’10 User’s latent feature(taste) influenced by social friends Handle trust propagation in social network

Using whole trust network for item rating prediction

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Proposed Improvements for Current Social Recommender

Social networks include multiple circles A more refined social trust information—richer

information Want to incorporate circle information into Social

Recommender Ideally, use trust circles specific to an item category

when predict rating in this category• E.g. Trust Circle of “Music”, Trust Circle of “Cars”, etc

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Proposed Improvements for Current Social Recommender

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Existing circles(google+, facebook) not corresponding to an item category

Proposed Improvements for Current Social Recommender

In existing multi-category rating datasets, no circle information

User trusts different subsets of friends in different domains(Cars, Music…)

User trusts different friends differently, related to friend’s expertise value

Should use trust circle specific to item category8

Outline Background & Motivation

Circle-based RS Trust Circle Inference Trust Value Assignment Model Training

EvaluationConclusion & Future work

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Trust Circle Inference User v is in inferred circle c of u iff u trust v in original social

network and both of them have rating in category c

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Original Social Network

Inferred circle for category C1

Inferred circle for category C2

Inferred circle for category C3

Trust Value Assignment

CircleCon1: Equal Trust each user in the inferred circle gets assigned

the same trust value

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Trust Value Assignment CircleCon2: Expertise-based Trust

assign a higher trust value or weight to the friends that are experts in the circle / category.

Variant a:• Expertise value of user u proportional to u’s number of

ratings in a circle

Variant b:• Expertise based on u’s number of rating in circle and

voting value from u’s followers in this circle

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CircleCon3: Trust Splitting

Most trust due to followee’s rating in one category Likelihood u2 trusts u1 in C1, C2 ? Infer likelihood proportional on u2’s number of

ratings in C1 and C2. Assign trust value in a category proportional to the

likelihood u2 trusts u1 in a category

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Original trust link trust link in c1 trust link in c2

CircleCon3: Trust Splitting

Normalize across followees

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Outline Background & Motivation

Circle-based RS Trust Circle Inference Trust Value Assignment Model Training

EvaluationConclusion & Future work

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Model Training Training with ratings from each category

Predict user’s rating in category c Input rating: rating in category c Input social network: Circle c

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is the number of items in category c

Solved by gradient descent

is social information weight

Model TrainingTraining with ratings for all categories

Predict user’s rating in category c Input rating: rating from all categories Input social network: Circle c

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Outline Background & Motivation

Circle-based RS Trust Circle Inference Trust Value Assignment Model Training

EvaluationConclusion & Future work

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Epinions Data

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Performance Metrics

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Training with per-category ratings

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Training with per-category ratings

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Training with ratings from all categories

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CircleCon3 of training with per-category rating

Conclusions Propose a novel Circle-based Social

Recommendation framework Split original social network to different circles, one circle

corresponding to one item category User trusts different subsets of friends in different

domains(Cars, Music…) User trusts different friends differently, based on friend’s

expertise

Outperforms the state-of-the-art social collaborative filtering algorithms

Show the promising future of circle-construction techniques in Social Recommender

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Thanks!

Q & A

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Trust Value Assignment

CircleCon1: Equal Trust

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CircleCon2: Expertise-based Trust

Variant a: Expertise based on number of rating in a circle

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CircleCon2: Expertise-based Trust Variant b:

Expertise value based on user’s number of rating in circle and voting value from followers in this circle

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Dw records the proportions of ratings user w assigned in all categories. It reflects the interest distribution of w cross all categories

( ) ( ) ( )c c cv v vE N

Training with ratings from all categories

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Training with ratings from all categories

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