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Recsys Presentation Recsys Presentation Presentation Transcript

  • 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]
  • outline
    • Background
    • Motivation – Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion – Future Work
    • project utiforo
    • “ pervasive support for market trading”
    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) Step 2 : k-Nearest Neighbours Step 3 : Recommendation Aggregation similarity
  • 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? solution: estimate profile similarity with privacy
  • outline
    • Background
    • Defining Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion – Future Work
  • 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 The mean rating r mean of user a The number of items user a has rated
  • 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
    • Background
    • Defining Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion
  • 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
  • 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
  • concordance: definition measure similarity according to proportion of agreement: somers’ d:
  • accuracy coverage vs. compare performance using netflix data subset
  • outline
    • Background
    • Defining Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion
  • 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
    • Background
    • Defining Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion
  • 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 upper/lower bounds of overlap (full details in paper)
  • 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
  • outline
    • Background
    • Defining Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion
  • evaluation
    • simulated profiles:
    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
  • sparsity effect – size effect
  • 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..
  • accuracy -- coverage
  • outline
    • Background
    • Defining Privacy
    • Concordance
    • Transitivity of Concordance
    • Private Collaborative Filtering
    • Evaluation
    • Conclusion – Future Work
  • future work
    • full analysis of concordance -based similarity measure
    analysis of the effect of correlation coefficients on communities of recommenders … towards trust-based distributed recommender systems
  • 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