Private Distributed Collaborative Filtering

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

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  • Private Distributed Collaborative Filtering

    1. 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. 2. Outline <ul><li>Background </li></ul><ul><li>Motivation – Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity of Concordance </li></ul><ul><li>Private Collaborative Filtering </li></ul><ul><li>Evaluation </li></ul><ul><li>Conclusion – Future Work </li></ul>
    3. 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. 4. Who do you trust? <ul><li>Distributed, peer-to-peer recommender system scenario: </li></ul><ul><li>How do we bootstrap cooperation when we do not know how much to trust the community? </li></ul><ul><ul><li>Estimate profile similarity with privacy </li></ul></ul><ul><ul><li>Cooperation? </li></ul></ul><ul><ul><li>Malicious behaviour? </li></ul></ul>Trust?
    5. 5. Privacy: 2 views privacy Controlling the flow of personal information The right to be “left alone” (out of public view)
    6. 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. 7. Public Information The total number of items that can be rated A recommendation ( r a,i – r mean ) context: collaboration:
    8. 8. Problem? similarity measures are not transitive
    9. 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. 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. 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. 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. 13. Concordance: Definition “ Trainspotting” x y Somers’ d: “ Transformers” x y “ The Godfather” x y … … … measure similarity according to proportion of agreement:
    14. 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. 15. Accuracy
    16. 16. Coverage
    17. 17. Problem? similarity measures are not transitive is agreement transitive? four examples:
    18. 18. Transitivity of Concordance: Four Examples (1) tied concordant 0.0 0.4 1.2 ms. green and mr. blue are: tied
    19. 19. Transitivity of Concordance: Four Examples (2) concordant 0.8 0.4 1.2 concordant ms. green and mr. blue are: concordant
    20. 20. Transitivity of Concordance: Four Examples (3) discordant 0.8 - 0.4 1.2 discordant ms. green and mr. blue are: concordant
    21. 21. Transitivity of Concordance: Four Examples (4) concordant - 0.8 0.4 1.2 discordant ms. green and mr. blue are: discordant
    22. 22. Transitivity of Concordance: Result D T T T T T T T C C C C D D D
    23. 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. 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. 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. 26. Discordant Pairs: 
    27. 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. 28. Does this preserve privacy? <ul><li>worst case scenario: full profile disclosed </li></ul>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. 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. 30. Evaluation: <ul><li>simulated profiles </li></ul>The estimation should be independent of what the ratings actually are: 1)
    31. 31. Sparsity Effect
    32. 32. Size Effect
    33. 33. Evaluation: Simulated Profiles Highest error when dataset is: small and very sparse How well do estimated coefficients work to generate recommendations ? 2)
    34. 34. Accuracy
    35. 35. Coverage
    36. 36. Future Work <ul><li>full analysis of concordance -based similarity measures (better evaluation!) </li></ul>analysis of the effect of correlation coefficients on communities of recommenders related work, research: mobblog.cs.ucl.ac.uk
    37. 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]

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