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Presentation based on paper "Private Distributed Collaborative Filtering Using Estimated Concordance Measures"

Presentation based on paper "Private Distributed Collaborative Filtering Using Estimated Concordance Measures"

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Private Distributed Collaborative Filtering Presentation Transcript

  • 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]
  • 2. Outline
    • Background
    • Motivation – Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion – Future Work
  • 3. Collaborative Filtering: Background 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
  • 4. Who do you trust?
    • Distributed, peer-to-peer recommender system scenario:
    • How do we bootstrap cooperation when we do not know how much to trust the community?
      • Estimate profile similarity with privacy
      • Cooperation?
      • Malicious behaviour?
    Trust?
  • 5. Privacy: 2 views privacy Controlling the flow of personal information The right to be “left alone” (out of public view)
  • 6. 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
  • 7. Public Information The total number of items that can be rated A recommendation ( r a,i – r mean ) context: collaboration:
  • 8. Problem? similarity measures are not transitive
  • 9. Concordance: Definition Movie Title x y define : d a,i = r a,i - r mean measure similarity according to proportion of agreement: classify ratings into one of three groups
  • 10. Concordance: Definition “ Waking Life” x y d a,i > 0 and d b,i > 0 concordant : we agree or d a,i < 0 and d b,i < 0
  • 11. Concordance: Definition “ Terminator” x y d a,i > 0 and d b,i < 0 discordant : we dis agree or d a,i < 0 and d b,i > 0
  • 12. Concordance: Definition “ Airplane!” x y d a,i = 0 tied : one of us has no opinion d b,i = 0 one of us has not rated the item
  • 13. Concordance: Definition “ Trainspotting” x y Somers’ d: “ Transformers” x y “ The Godfather” x y … … … measure similarity according to proportion of agreement:
  • 14. Somers’ d vs. Pearson Correlation Coefficient compare performance using netflix data subset 999 users 100 – 500 ratings per user 17,770 movies vs.
  • 15. Accuracy
  • 16. Coverage
  • 17. Problem? similarity measures are not transitive is agreement transitive? four examples:
  • 18. Transitivity of Concordance: Four Examples (1) tied concordant 0.0 0.4 1.2 ms. green and mr. blue are: tied
  • 19. Transitivity of Concordance: Four Examples (2) concordant 0.8 0.4 1.2 concordant ms. green and mr. blue are: concordant
  • 20. Transitivity of Concordance: Four Examples (3) discordant 0.8 - 0.4 1.2 discordant ms. green and mr. blue are: concordant
  • 21. Transitivity of Concordance: Four Examples (4) concordant - 0.8 0.4 1.2 discordant ms. green and mr. blue are: discordant
  • 22. Transitivity of Concordance: Result D T T T T T T T C C C C D D D
  • 23. 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
  • 24. a b c d r1 r2 r3 r4 a b c d r1 r2 r3 r4 Tied Pairs: Upper Bound : None of the tied pairs overlap Lower Bound : All the tied pairs overlap Tied Concordant Discordant
  • 25. a b c d r1 r2 r3 r4 a b c d r1 r2 r3 r4 Concordant Pairs: Upper Bound : Maximum overlap of concordant pairs plus minimum overlap of discordant pairs Lower Bound : Minimum overlap of concordant pairs Tied Concordant Discordant
  • 26. Discordant Pairs: 
  • 27. Does this preserve privacy? 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 warning : potential inference
  • 28. Does this preserve privacy?
    • worst case scenario: full profile disclosed
    a b c d 4 3 3 4 a b c d 5 3 2 4 C C C C solution? collaboratively create random set
  • 29. Evaluation How well does this method estimate the actual coefficients? How well do estimated coefficients work to generate recommendations ? on all datasets? 1) 2)
  • 30. Evaluation:
    • simulated profiles
    The estimation should be independent of what the ratings actually are: 1)
  • 31. Sparsity Effect
  • 32. Size Effect
  • 33. Evaluation: Simulated Profiles Highest error when dataset is: small and very sparse How well do estimated coefficients work to generate recommendations ? 2)
  • 34. Accuracy
  • 35. Coverage
  • 36. Future Work
    • full analysis of concordance -based similarity measures (better evaluation!)
    analysis of the effect of correlation coefficients on communities of recommenders related work, research: mobblog.cs.ucl.ac.uk
  • 37. 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]