Private Distributed Collaborative Filtering Using Estimated Concordance Measures Neal Lathia Dr. Stephen Hailes Dr. Licia ...
outline <ul><li>Background </li></ul><ul><li>Motivation – Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transiti...
<ul><li>project  utiforo </li></ul><ul><li>“ pervasive support for market trading” </li></ul>what should I buy?
problem: i need  recommendations: peer-to-peer distributed
solution: distributed collaborative filtering a b c d 4 3 3 ? a b c d 4 ? 3 4 Step 1 : Profile Similarity (Correlation) St...
problem: who do you  trust? thief? uncooperative? spammer?
problem: who do you  trust? how do we bootstrap cooperation   when we do not know how much to trust the community? solutio...
outline <ul><li>Background </li></ul><ul><li>Defining Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity...
privacy… …  the right to be  left alone
privacy… …  the right to  control the flow of your personal information
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...
public information total number of  items a recommendation context: collaboration:
but even if we did trust some people… … similarity measures are not transitive ? privacy
outline <ul><li>Background </li></ul><ul><li>Defining Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity...
concordance: definition define : d a,i  = r a,i  -  r mean measure  similarity  according to proportion of  agreement: cla...
concordance: definition concordant : we agree (C) discordant : we  dis agree (D) tied : one of us has  no opinion  (T) +1 ...
concordance: definition measure  similarity  according to proportion of  agreement: somers’ d:
accuracy coverage vs. compare performance using  netflix  data subset
outline <ul><li>Background </li></ul><ul><li>Defining Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity...
concordance-based similarity: … agreement is transitive! ? concordant discordant privacy
concordance-based similarity: … agreement is transitive! discordant concordant discordant privacy
concordance-based similarity: … agreement is transitive! ? discordant discordant privacy
concordance-based similarity: … agreement is transitive! concordant discordant discordant privacy
transitivity of concordance: result D T T T T T T T C C C C D D D
outline <ul><li>Background </li></ul><ul><li>Defining Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity...
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
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 upp...
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...
outline <ul><li>Background </li></ul><ul><li>Defining Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity...
evaluation <ul><li>simulated  profiles: </li></ul>How well does this method  estimate  the actual coefficients? How well d...
sparsity effect – size effect
evaluation: Highest error when dataset is:  small  and  very sparse How well do estimated coefficients work to  generate r...
accuracy -- coverage
outline <ul><li>Background </li></ul><ul><li>Defining Privacy </li></ul><ul><li>Concordance </li></ul><ul><li>Transitivity...
future work <ul><li>full analysis of concordance -based similarity measure </li></ul>analysis of the  effect of correlatio...
Private Distributed Collaborative Filtering using Estimated Concordance Measures Neal Lathia Dr. Stephen Hailes Dr. Licia ...
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    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. <ul><li>project utiforo </li></ul><ul><li>“ pervasive support for market trading” </li></ul>what should I buy?
    4. 4. problem: i need recommendations: peer-to-peer distributed
    5. 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. 6. problem: who do you trust? thief? uncooperative? spammer?
    7. 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
    8. 8. outline <ul><li>Background </li></ul><ul><li>Defining 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>
    9. 9. privacy… … the right to be left alone
    10. 10. privacy… … the right to control the flow of your personal information
    11. 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. 12. public information total number of items a recommendation context: collaboration:
    13. 13. but even if we did trust some people… … similarity measures are not transitive ? privacy
    14. 14. outline <ul><li>Background </li></ul><ul><li>Defining 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 </li></ul>
    15. 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. 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. 17. concordance: definition measure similarity according to proportion of agreement: somers’ d:
    18. 18. accuracy coverage vs. compare performance using netflix data subset
    19. 19. outline <ul><li>Background </li></ul><ul><li>Defining 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 </li></ul>
    20. 20. concordance-based similarity: … agreement is transitive! ? concordant discordant privacy
    21. 21. concordance-based similarity: … agreement is transitive! discordant concordant discordant privacy
    22. 22. concordance-based similarity: … agreement is transitive! ? discordant discordant privacy
    23. 23. concordance-based similarity: … agreement is transitive! concordant discordant discordant privacy
    24. 24. transitivity of concordance: result D T T T T T T T C C C C D D D
    25. 25. outline <ul><li>Background </li></ul><ul><li>Defining 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 </li></ul>
    26. 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. 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)
    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. outline <ul><li>Background </li></ul><ul><li>Defining 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 </li></ul>
    30. 30. evaluation <ul><li>simulated profiles: </li></ul>How well does this method estimate the actual coefficients? How well do estimated coefficients work to generate recommendations ? on all datasets? 1) 2) inputs: sparsity, size
    31. 31. sparsity effect – size effect
    32. 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. 33. accuracy -- coverage
    34. 34. outline <ul><li>Background </li></ul><ul><li>Defining 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>
    35. 35. future work <ul><li>full analysis of concordance -based similarity measure </li></ul>analysis of the effect of correlation coefficients on communities of recommenders … towards trust-based distributed recommender systems
    36. 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

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