1. Private Distributed Collaborative Filtering Using Estimated Concordance Measures Neal Lathia Dr. Stephen Hailes Dr. Licia Capra Department of Computer Science University College London [email_address]

4. problem: i need recommendations: peer-to-peer distributed

5. solution: distributed collaborative filtering a b c d 4 3 3 ? a b c d 4 ? 3 4 Step 1 : Profile Similarity (Correlation) Step 2 : k-Nearest Neighbours Step 3 : Recommendation Aggregation similarity

6. problem: who do you trust? thief? uncooperative? spammer?

7. problem: who do you trust? how do we bootstrap cooperation when we do not know how much to trust the community? solution: estimate profile similarity with privacy

9. privacy… … the right to be left alone

10. privacy… … the right to control the flow of your personal information

11. private information a b c d 4 ? 3 4 A rating r a,i by user a for item i The full set of ratings r a for user a The mean rating r mean of user a The number of items user a has rated

12. public information total number of items a recommendation context: collaboration:

13. but even if we did trust some people… … similarity measures are not transitive ? privacy

15. concordance: definition define : d a,i = r a,i - r mean measure similarity according to proportion of agreement: classify ratings into one of three groups

16. concordance: definition concordant : we agree (C) discordant : we dis agree (D) tied : one of us has no opinion (T) +1 +3 -2.7 +1.5

17. concordance: definition measure similarity according to proportion of agreement: somers’ d:

18. accuracy coverage vs. compare performance using netflix data subset

20. concordance-based similarity: … agreement is transitive! ? concordant discordant privacy

21. concordance-based similarity: … agreement is transitive! discordant concordant discordant privacy

22. concordance-based similarity: … agreement is transitive! ? discordant discordant privacy

23. concordance-based similarity: … agreement is transitive! concordant discordant discordant privacy

24. transitivity of concordance: result D T T T T T T T C C C C D D D

26. private collaborate filtering: the idea a b c d 4 3 3 ? a b c d 4 ? 3 4 a b c d 5 3 2 4 C, D, T C, D, T C, D, T

27. private collaborate filtering: the idea a b c d 4 3 3 ? a b c d 4 ? 3 4 a b c d 5 3 2 4 C, D, T estimate similarity by upper/lower bounds of overlap (full details in paper)

31. sparsity effect – size effect

32. evaluation: Highest error when dataset is: small and very sparse How well do estimated coefficients work to generate recommendations ? 2) return to the netflix dataset..

33. accuracy -- coverage

36. Private Distributed Collaborative Filtering using Estimated Concordance Measures Neal Lathia Dr. Stephen Hailes Dr. Licia Capra Department of Computer Science University College London [email_address] related work, research: mobblog.cs.ucl.ac.uk