Recommendation in Social Networks

1. MohsenJamali PhD Depth Examination Simon Fraser University January 2010 Recommendation in Social Networks

2. Outline • Introduction • Collaborative Filtering • Memory-based • Model-based • Recommendation in Social Networks • Memory-based • Model-based • Conclusion Recommendation in Social Networks

3. Introduction • Need for Recommenders We are leaving the age of information and entering the age of recommendation Chris Anderson, The Long Tail: Why the Future of Business Is Selling Less of More

4. Tasks in Recommender Systems • 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 Mohsen Jamali, Recommendation in Social Networks

5. Introduction – Categories of RSs • Content-based recommender systems • The contents of items and user profiles are available. • Prediction is accomplished based on the ratings on similar items. • Collaborative Filtering • Input: Rating Matrix • Hybrid • Combining CF and Content based. Recommendation in Social Networks

6. Introduction - 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 Recommendation in Social Networks

7. Introduction - Recommendation in Social Networks • Not only the rating profiles, but also the social relations are considered in SN based RSs Recommendation in Social Networks

8. Introduction – Evaluating Recommender System • Cross-Fold Validation • Leave-one-out Recommendation in Social Networks

9. Outline • Introduction • Collaborative Filtering • Memory-based • Model-based • Recommendation in Social Networks • Memory-based • Model-based • Conclusion Recommendation in Social Networks

10. Memory based vs. Model based • Memory based Recommendation • Explores the rating matrix to compute the prediction • Slow in prediction • Model based Recommendation • Learns a model • Stores only the parameters of the model • Faster prediction • Extra Learning phase Recommendation in Social Networks

11. Memory-based CF • User-based CF • Considers Users with Similar Rating Patterns • Aggregates the ratings of Similar Users • Similarity: Cosine similarity, Pearson Correlation • Challenges with User-based CF • Sparsity • Scalability Recommendation in Social Networks

12. Memory-based CF (cont) • Item-based CF • Considers items already rated by the user • Aggregates their ratings to compute prediction • Aggregation weights: similarity of rating patterns • More users compared to items • Item-based is more scalable • [Sarwar, et.al. 2001] experimentally showed that items based approach has comparable results to user-based approach Recommendation in Social Networks

13. Model-based CF • Clustering based Recommendation • Cluster the users • Similar to user-based CF, but the neighborhood is the cluster the user belongs to. • Association Rule based approach • Usually for binary ratings • Random Walk based method [Yildirimet.al. 2008] • Several random walks on the item graph • starting from items rated by the user Recommendation in Social Networks

14. Model-based CF (cont) • Matrix Factorization • Factorize the rating matrix into product of user and item latent variables • Uu: user latent variable • Vi: Item latent variable Recommendation in Social Networks

15. Model-based CF (cont) • Matrix Factorization (cont) Recommendation in Social Networks

16. Outline • Introduction • Collaborative Filtering • Memory-based • Model-based • Recommendation in Social Networks • Memory-based • Model-based • Conclusion Recommendation in Social Networks

17. Recommendation in Social Networks Recommendation in Social Networks

18. Recommendation in Social Networks • Main motivations for SN based RS • Cold start users (50% of users) • Independent available information • Exploiting social influence Recommendation in Social Networks

19. Recommendation in Social Networks • TidalTrust [Golbeck 2005] • BFS • Considers all raters at the shortest distance • Aggregate the ratings of raters Recommendation in Social Networks

20. Recommendation in Social Networks • MoleTrust [Massa et.al 2007] • Similar to TidalTrust • Maximum-depth • Trust-Noise Trade-off Recommendation in Social Networks

21. Recommendation in Social Networks • Social Trust Ensemble (STE) [Ma et.al. 2009] • Matrix factorization • Linear combination of basic MF and social network based approach Recommendation in Social Networks

22. Recommendation in Social Networks • Social Trust Ensemble (STE) Recommendation in Social Networks

23. Conclusion and Open Problems • Importance of social network based approaches for recommendation • Cold start users • Sparsity of the data sets • Trade-off between noise and reliability • Explaining the Recommendation • Top-N Recommendation and Link Prediction • Do we need the social network for all users? Recommendation in Social Networks

24. Thanks! ? Recommendation in Social Networks

25. Recommendation in Social Networks • Advogato [Levien et.al. 2002] • Maximum-flow trust metric • Input • N: the number of users to trust • x: the source user. Recommendation in Social Networks

26. Recommendation in Social Networks • Advogato (cont) Recommendation in Social Networks

27. Recommendation in Social Networks • Advogato (cont) Recommendation in Social Networks

28. Recommendation in Social Networks • Apple Seed [Ziegler 2005] • Spreading activation models • Source node is activated with energy e. • The energy is propagated through the network. • Nodes that receive more energy are trustworthy Recommendation in Social Networks

29. Top-N Recommendation • [Deshpandeet.al 2004] Extending Item CF Recommendation in Social Networks