Social Recommendation

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Social Networks Emerged Recently
Independent source of information
Motivation of SN-based RS
Social Influence: users adopt the
behavior of their friends

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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Target Customer
Aggregator
Prediction
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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


Social Rating Network
Social Network Trust Network
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Cold Start users
Very few ratings 50% of users Main target of SN
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
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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Aggregator
Prediction Mohsen Jamali, Recommendation in Social Networks
List of Top Movies ??
Recommender
Movie 1 Movie 2
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Movie 3
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Introduction
Collaborative Filtering Social Recommendation Evaluating Recommenders
Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada
UBC Data Mining Lab October 2010

Introduction
Collaborative Filtering Social Recommendation Evaluating Recommenders
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How Far to Go into Network?
Tradeoff between Precision and Recall
Trusted friends on similar items

Predicting the rating on a target item for a given user (i.e. Predicting John’s rating on Star Wars Movie). movie1 ??
Recommender

Recommending a List of items to a given user (i.e. Recommending a list of movies to John for watching).
http://www.cs.sfu.ca/~sja25/personal/datasets/
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General Statistics of Flixster and Epinions Flixster: 1M users, 47K items
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Cross Validation
K-Fold Leave-one-out

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)
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Far neighbors on the exact target item
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TrustWalker
Random Walk Model Combines Item-based Recommendation and

Epinions – public domain Flixster
is a social networking
service for movie rating The crawled data set includes data from Nov 2005 – Nov 2009 Available at



TrustWalker SocialMF Conclusion
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Need For Recommenders
Rapid Growth of Information Lots of Options for Users



TrustWalker SocialMF Conclusion
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Introduction
Collaborative Filtering Social Recommendation Evaluating Recommenders
recommenders
A Sample Social Rating Network
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Classification of Recommenders
Memory based Model based
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Issues in Trust-based Recommendation
Noisy
data in far distances Low probability of Finding rater at close distances

Memory based approaches for recommendation in social networks
[Golbeck, 2005] [Massa et.al. 2007] [Jamali et.al. 2009]
[Ziegler, 2005]
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Introduction
Collaborative Filtering Social Recommendation Evaluating Recommenders



TrustWalker SocialMF Conclusion
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Introduction
Collaborative Filtering Social Recommendation Evaluating Recommenders



TrustWalker SocialMF Conclusion



TrustWalker SocialMF Conclusion
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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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Introduction
Collaborative Filtering Social Recommendation Evaluating Recommenders



Байду номын сангаас
TrustWalker SocialMF Conclusion
Mohsen Jamali, Recommendation in Social Networks
With 1- Φu,i,k , continue the random walk to a
direct neighbor of u.
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Item Similarities

Φu,i,k
Similarity of items rated by u and target item i.
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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 .
Explores the trust network to find Raters. Aggregate the ratings from raters for prediction. Different weights for users

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