Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada UBC Data Mining Lab October...

Recommendation in Social Networks

Mohsen Jamali, Martin EsterSimon Fraser UniversityVancouver, Canada

UBC Data Mining Lab October 2010

Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 2

Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 3

Introduction

Need For Recommenders Rapid Growth of Information Lots of Options for Users

Input Data A set of users U={u1, …, uN}

A set of items I={i1, …, iM}

The rating matrix R=[ru,i]NxM

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Problem Definitions in RSs

Predicting the rating on a target item for a given user (i.e. Predicting John’s rating

on Star Wars Movie).

Recommending a List of items to a given user (i.e. Recommending a list of

movies to John for watching).

movie1 ??Recommender

List of Top Movies ??

Recommender

Movie 1

Movie 2

Movie 35Mohsen Jamali, Recommendation in Social Networks

Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 6

Collaborative Filtering

Most Used and Well Known Approach for Recommendation

Finds Users with Similar Interests to the target User

Aggregating their opinions to make a recommendation.

Often used for the prediction task

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Collaborative Filtering

TargetCustomer

Aggregator

Prediction

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Item based Collaborative Filtering

Normally, there are a lot more users than items

Collaborative Filtering doesn’t scale well with users

Item based Collaborative Filtering has been proposed in 2001

They showed that the quality of results are compatible in item based CF

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Item-based Collaborative Filtering

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Item-Item Collaborative Filtering

Aggregator

Prediction 11Mohsen Jamali, Recommendation in Social Networks

Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 12

Recommendation in Social Networks

Social Networks Emerged Recently Independent source of information

Motivation of SN-based RS Social Influence: users adopt the

behavior of their friends Social Rating Network

Social Network Trust Network

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Recommendation in Social Networks

Cold Start users Very few ratings 50% of users Main target of SN

recommenders

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A Sample Social Rating Network

Recommendation in Social Networks

Classification of Recommenders Memory based Model based

Memory based approaches for recommendation in social networks [Golbeck, 2005] [Massa et.al. 2007] [Jamali et.al. 2009] [Ziegler, 2005]

Mohsen Jamali, Recommendation in Social Networks 15

Trust-based Recommendation

Explores the trust network to find Raters.

Aggregate the ratings from raters for prediction.

Different weights for users

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Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 17

Evaluating Recommenders

Cross Validation K-Fold Leave-one-out

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)

Mohsen Jamali, Recommendation in Social Networks 18

Data Sets

Epinions – public domain Flixster

Flixster.com is a social networking service for movie rating

The crawled data set includes data from Nov 2005 – Nov 2009

Available at http://www.cs.sfu.ca/~sja25/personal/datasets/

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Data Sets (cont.)

Mohsen Jamali, Recommendation in Social Networks 20

General Statistics of Flixster and Epinions

Flixster: 1M users, 47K items 150K users with at least one rating Items: movies 53% cold start

Epinions: 71K users, 108K items Items: DVD Players, Printers, Books,

Cameras,… 51% cold start

Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 21

TrustWalker - Motivation

Issues in Trust-based Recommendation Noisy data in far

distances Low probability of

Finding rater at close distances

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TrustWalker - Motivation

How Far to Go into Network? Tradeoff between Precision and Recall

Trusted friends on similar items

Far neighbors on the exact target item

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TrustWalker

TrustWalker Random Walk Model Combines Item-based Recommendation

and Trust-based Recommendation Random Walk

To find a rating on the exact target item or a similar item

Prediction = returned rating

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Single Random Walk

Starts from Source user u0. At step k, at node u:

If u has rated I, return ru,i

With Φu,i,k , the random walk stops▪ Randomly select item j rated by u and return

ru,j .

With 1- Φu,i,k , continue the random walk to a direct neighbor of u.

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Stopping Probability in TrustWalker

Item Similarities

Φu,i,k

Similarity of items rated by u and target item i.

The step of random walk

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Recommendation in TrustWalker Prediction = Expected value of rating returned by

random walk.

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Properties of TrustWalker

Special Cases of TrustWalker Φu,i,k = 1▪ Random Walk Never Starts.▪ Item-based Recommendation.

Φu,i,k = 0▪ Pure Trust-based Recommendation.▪ Continues until finding the exact target item.▪ Aggregates the ratings weighted by probability of reaching them.▪ Existing methods approximate this.

Confidence How confident is the prediction

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Experimental Setups

Evaluation method Leave-one-out

Evaluation Metrics RMSE Coverage Precision = 1- RMSE/4

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Comparison Partners

Tidal Trust [Golbeck, 2005] Mole Trust [Massa, 2007] CF Pearson Random Walk 6,1 Item-based CF TrustWalker0 [-pure] TrustWalker [-pure]

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Experiments – Cold Start Users

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Experiment- All users

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Experiments - Confidence

More confident Predictions have lower error

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Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 34

Matrix Factorization

Model based approach Latent features for users

Latent features for items

• Ratings are scaled to [0,1]• g is logistic function

Mohsen Jamali, Recommendation in Social Networks 35

U and V have normal priors

Social Trust Ensemble [2009]

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Social Trust Ensemble (cont.)

Issues with STE Feature vectors of neighbors should

influence the feature vector of u not his ratings

STE does not handle trust propagation Learning is based on observed ratings

only.

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The SocialMF Model

Social Influence behavior of a user u is affected by his direct neighbors Nu.

Latent characteristics of a user depend on his neighbors.

Tu,v is the normalized trust value.

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The SocialMF Model (cont.)

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The SocialMF Model (cont.)

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The SocialMF Model (cont.)

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The SocialMF Model (cont.)

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The SocialMF Model (cont.)

Mohsen Jamali, Recommendation in Social Networks 43

The SocialMF Model (cont.)

Properties of SocialMF Trust Propagation User latent feature learning possible with

existence of the social network▪ No need to fully observed rating for learning▪ Appropriate for cold start users

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Experimental Setups

5-fold cross validation Using RMSE for evaluation Comparison Partners

Basic MF STE CF

Model parameters SocialMF: STE:

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Results for Epinions

Gain over STE: 6.2%. for K=5 and 5.7% for K=10

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CF BaseMF STE SocialMF1

1.021.041.061.08

1.11.121.141.161.18

1.2

k=5k=10R

MS

E

Results for Flixster

SocialMF gain over STE (5%) is 3 times the STE gain over BasicMF (1.5%)

Mohsen Jamali, Recommendation in Social Networks 47

CF BaseMF STE SocialMF0.76

0.78

0.8

0.82

0.84

0.86

0.88

0.9

0.92

k=5k=10R

MS

E

Results (cont.)

Lower error for Flixster

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CF BaseMF STE SocialMF0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

k=5k=10k=5k=10

RM

SE Epinio

ns

Flixster

Sensitivity Analysis on λT

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Sensitivity Analysis for Epinions

Sensitivity Analysis on λT

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Sensitivity Analysis for Flixster

Experiments on Cold Start Users

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RMSE values on cold start users (K=5)

CF BaseMF STE SocialMF1

1.1

1.2

1.3

1.4

Epinions

Experiments on Cold Start Users

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RMSE values on cold start users (K=5)

CF BaseMF STE SocialMF0.95

11.051.1

1.151.2

1.25

Flixster

Experiments on Cold Start Users

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Flixster Epinions

Cold Start Users 0.085 0.115

All Users 0.05 0.062

-1.00%

1.00%

3.00%

5.00%

7.00%

9.00%

11.00%

RMSE Gain of SocialMF over STE

Analysis of Learning Runtime

SocialMF: STE: SocialMF is faster by factor

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N # of Users

K Latent Feature Size

Avg. ratings per user

Avg. neighbors per user

rt

Outline

Introduction Collaborative Filtering Social Recommendation Evaluating Recommenders

TrustWalker SocialMF Conclusion

Mohsen Jamali, Recommendation in Social Networks 55

Conclusion

TrustWalker [KDD 2009] Memory-based Random walk approach

SocialMF [RecSys 2010] Model based Matrix Factorization approach

Other work Top-N Recommendation (RecSys 2009) Link Prediction (ACM TIST 2010)

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Conclusion

Future Work Framework for Clustering, Rating and

Link Prediction Explaining the recommendations Constructing the social network from

observed data.

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Mohsen Jamali, Recommendation in Social Networks 58

Thank you!