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
4Mohsen Jamali, Recommendation in Social Networks
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
7Mohsen Jamali, Recommendation in Social Networks
Collaborative Filtering
TargetCustomer
Aggregator
Prediction
8Mohsen Jamali, Recommendation in Social Networks
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
9Mohsen Jamali, Recommendation in Social Networks
Item-based Collaborative Filtering
10Mohsen Jamali, Recommendation in Social Networks
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
Mohsen Jamali, Recommendation in Social Networks 13
Recommendation in Social Networks
Cold Start users Very few ratings 50% of users Main target of SN
recommenders
Mohsen Jamali, Recommendation in Social Networks 14
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
16Mohsen Jamali, Recommendation in Social Networks 16
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/
Mohsen Jamali, Recommendation in Social Networks 19
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
22Mohsen Jamali, Recommendation in Social Networks 22
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
23Mohsen Jamali, Recommendation in Social Networks 23
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
24Mohsen Jamali, Recommendation in Social Networks 24
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.
25Mohsen Jamali, Recommendation in Social Networks 25
Stopping Probability in TrustWalker
Item Similarities
Φu,i,k
Similarity of items rated by u and target item i.
The step of random walk
26Mohsen Jamali, Recommendation in Social Networks
Recommendation in TrustWalker Prediction = Expected value of rating returned by
random walk.
27Mohsen Jamali, Recommendation in Social Networks
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
28Mohsen Jamali, Recommendation in Social Networks
Experimental Setups
Evaluation method Leave-one-out
Evaluation Metrics RMSE Coverage Precision = 1- RMSE/4
29Mohsen Jamali, Recommendation in Social Networks
Comparison Partners
Tidal Trust [Golbeck, 2005] Mole Trust [Massa, 2007] CF Pearson Random Walk 6,1 Item-based CF TrustWalker0 [-pure] TrustWalker [-pure]
30Mohsen Jamali, Recommendation in Social Networks 30
Experiments – Cold Start Users
31Mohsen Jamali, Recommendation in Social Networks
Experiment- All users
32Mohsen Jamali, Recommendation in Social Networks
Experiments - Confidence
More confident Predictions have lower error
33Mohsen Jamali, Recommendation in Social Networks
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]
Mohsen Jamali, Recommendation in Social Networks 36
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.
Mohsen Jamali, Recommendation in Social Networks 37
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.
Mohsen Jamali, Recommendation in Social Networks 38
The SocialMF Model (cont.)
Mohsen Jamali, Recommendation in Social Networks 39
The SocialMF Model (cont.)
Mohsen Jamali, Recommendation in Social Networks 40
The SocialMF Model (cont.)
Mohsen Jamali, Recommendation in Social Networks 41
The SocialMF Model (cont.)
Mohsen Jamali, Recommendation in Social Networks 42
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
Mohsen Jamali, Recommendation in Social Networks 44
Experimental Setups
5-fold cross validation Using RMSE for evaluation Comparison Partners
Basic MF STE CF
Model parameters SocialMF: STE:
Mohsen Jamali, Recommendation in Social Networks 45
Results for Epinions
Gain over STE: 6.2%. for K=5 and 5.7% for K=10
Mohsen Jamali, Recommendation in Social Networks 46
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
Mohsen Jamali, Recommendation in Social Networks 48
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
Mohsen Jamali, Recommendation in Social Networks 49
Sensitivity Analysis for Epinions
Sensitivity Analysis on λT
Mohsen Jamali, Recommendation in Social Networks 50
Sensitivity Analysis for Flixster
Experiments on Cold Start Users
Mohsen Jamali, Recommendation in Social Networks 51
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
Mohsen Jamali, Recommendation in Social Networks 52
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
Mohsen Jamali, Recommendation in Social Networks 53
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
Mohsen Jamali, Recommendation in Social Networks 54
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)
Mohsen Jamali, Recommendation in Social Networks 56
Conclusion
Future Work Framework for Clustering, Rating and
Link Prediction Explaining the recommendations Constructing the social network from
observed data.
Mohsen Jamali, Recommendation in Social Networks 57
Mohsen Jamali, Recommendation in Social Networks 58
Thank you!