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<ul><li>Exploring Recommender Systems: </li></ul>trust, graphs, and experts neal lathia telefonica i+d lunch seminar july ...
neal lathia (2nd yr phd): stephen hailes & licia capra department of computer science, university college london intern @ ...
<ul><li>warning: what follows is a slide mash-up from multiple sources </li></ul>
<ul><li>project   utiforo </li></ul><ul><li>“ pervasive support for market trading” </li></ul>what should I buy?
source: O. Celma & P. Lamere “Music Recommendation Tutorial” ISMIR 2007 recommender systems
<ul><li>collaborative filtering research   </li></ul>design methods to recommend stuff
…  a method to  classify content  correctly data    predicted ratings intelligent process our focus: k-nearest neighbour...
? kNN collaborative filtering 1. find neighbours 2. make predictions 3. recommend source: N. Lathia “Computing Recommendat...
e-commerce machine learning computer supported collaborative work human computer interaction mobile systems m-commerce age...
e-commerce machine learning computer supported collaborative work human computer interaction mobile systems m-commerce age...
distributed content dissemination via trustworthy peers filter out the spammers source: D. Quercia, S. Hailes, L. Capra “L...
problem: who do you  trust? thief? uncooperative? spammer?
bootstrap cooperation  with privacy source: N. Lathia, S. Hailes, L. Capra “Private Distributed Collaborative Filtering Us...
effect on simulated profiles High Estimation Error high correlation estimation error, but prediction accuracy remains?
similarity values depend on the method used: there is no agreement between measures [2] [3] [1] [5] [3] [4] [1] [3] [2] [3...
…  the pearson distribution  intelligent process
…  the modified pearson distributions weighted-PCC, constrained-PCC
…  and other measures  intelligent process somers’ d, co-rated, cosine angle
java.util.Random r = new java.util.Random() for all neighbours i { similarity(i) = (r.nextDouble()*2.0)-1.0); } …  what if...
accuracy   0.7811 0.7769 0.7773 0.8025 0.8073 0.7992 0.7718 459 0.8058 0.7992 0.7919 0.7679 0.7716 0.7771 0.7717 229 0.80...
a) our error measures are not good enough?
a) our error measures are not good enough?  b) is there something wrong with the dataset?
a) our error measures are not good enough?  b) is there something wrong with the dataset?  c) current user-similarity is n...
trust enables interaction in environments that are characterised by  uncertainty read more: N. Lathia “Learning to Trust o...
filtering is a  trust  problem weight users based on how  like-minded you have been to them in the past (similarity) to: w...
1. find neighbours 2. make predictions 4. get feedback 3. recommend trust engine risk engine source: N. Lathia, S. Hailes,...
(trust-based) select  valuable  neighbours
(trust-based) select  valuable  neighbours
(trust-based) select  valuable  neighbours a-symmetric value-added reward information
<ul><li>(movielens movie-rating data example) </li></ul>me you 26 18 4 0 0 5* 30 26 2 3 4* 19 10 1 1 4* 5 13 1 0 3* 2 11 2...
<ul><li>(movielens movie-rating data example) </li></ul>me you 26 18 4 0 0 5* 30 26 2 3 4* 19 10 1 1 4* 5 13 1 0 3* 2 11 2...
<ul><li>can we learn to interpret neighbour recommendations? </li></ul>me you 26 18 4 0 0 5* 30 26 2 3 4* 19 10 1 1 4* 5 1...
<ul><li>“ do what I  mean , not what I  say ” </li></ul>semantic distance (rating transpose)
no profile? trust  bootstrapping  [see: Massa et al] uncertain? award  potential  recommenders extensions? Note (a): a [co...
a user kNN algorithm generates a  graph source: N. Lathia, S. Hailes, L. Capra “kNN CF: A Temporal Social Network” To appe...
what properties does the graph have? <ul><li>growth </li></ul><ul><li>preferential attachment </li></ul><ul><li>converging...
similarity evolution
similarity evolution
what properties does the graph have? <ul><li>growth </li></ul><ul><li>preferential attachment </li></ul><ul><li>converging...
Note (b): a [kNN structure]  subset  of the community of users seems to be good recommenders for everybody.
<ul><li>Exploring Recommender Systems: </li></ul>the wisdom of the few
users items sparse ratings situation:
users items sparse ratings accuracy coverage uncertainty privacy noise complication:
users items sparse ratings more information one solution: (difficult)
users items sparse ratings more information our solution:
users items sparse ratings our solution: experts: rotten tomatoes flixster dense ratings items
training set items sparse ratings source set dense ratings test set
different characteristics (data)…
different characteristics (users)…
changing collaborative filtering before: find best  method data users ?
changing collaborative filtering now: find best  data source ? user-dependent problem can provably generate an RMSE < 0.85...
<ul><li>Exploring Recommender Systems: </li></ul>trust, graphs, and experts neal lathia [email_address] http://www.cs.ucl....
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Telefonica Lunch Seminar

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Transcript of "Telefonica Lunch Seminar"

  1. 1. <ul><li>Exploring Recommender Systems: </li></ul>trust, graphs, and experts neal lathia telefonica i+d lunch seminar july 8, 2008
  2. 2. neal lathia (2nd yr phd): stephen hailes & licia capra department of computer science, university college london intern @ telefonica i+d: xavier amatriain
  3. 3. <ul><li>warning: what follows is a slide mash-up from multiple sources </li></ul>
  4. 4. <ul><li>project utiforo </li></ul><ul><li>“ pervasive support for market trading” </li></ul>what should I buy?
  5. 5. source: O. Celma & P. Lamere “Music Recommendation Tutorial” ISMIR 2007 recommender systems
  6. 6. <ul><li>collaborative filtering research  </li></ul>design methods to recommend stuff
  7. 7. … a method to classify content correctly data   predicted ratings intelligent process our focus: k-nearest neighbours (kNN)
  8. 8. ? kNN collaborative filtering 1. find neighbours 2. make predictions 3. recommend source: N. Lathia “Computing Recommendations with Collaborative Filtering” To appear, Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling
  9. 9. e-commerce machine learning computer supported collaborative work human computer interaction mobile systems m-commerce agents artificial communities distributed systems trust management reputation systems social networks who does the research?
  10. 10. e-commerce machine learning computer supported collaborative work human computer interaction mobile systems m-commerce agents artificial communities distributed systems trust management reputation systems social networks example?
  11. 11. distributed content dissemination via trustworthy peers filter out the spammers source: D. Quercia, S. Hailes, L. Capra “Lightweight Distributed Trust Propagation” ICDM 2007
  12. 12. problem: who do you trust? thief? uncooperative? spammer?
  13. 13. bootstrap cooperation with privacy source: N. Lathia, S. Hailes, L. Capra “Private Distributed Collaborative Filtering Using Estimated Concordance Measures” RecSys 2007, Minn, USA method: estimate transitive similarity evaluation: netflix dataset & simulated profiles
  14. 14. effect on simulated profiles High Estimation Error high correlation estimation error, but prediction accuracy remains?
  15. 15. similarity values depend on the method used: there is no agreement between measures [2] [3] [1] [5] [3] [4] [1] [3] [2] [3]  my profile your profile  pearson -0.50 weighted- pearson -0.05 cosine angle 0.76 co-rated proportion 1.00 concordance -0.06 bad near zero good very good near zero
  16. 16. … the pearson distribution  intelligent process
  17. 17. … the modified pearson distributions weighted-PCC, constrained-PCC
  18. 18. … and other measures  intelligent process somers’ d, co-rated, cosine angle
  19. 19. java.util.Random r = new java.util.Random() for all neighbours i { similarity(i) = (r.nextDouble()*2.0)-1.0); } … what if..? source: N. Lathia, S. Hailes, L. Capra “The Effect of Correlation Coefficients on Communities of Recommenders” ACM SAC TRECK 2008, Fortaleza, Brazil.
  20. 20. accuracy  0.7811 0.7769 0.7773 0.8025 0.8073 0.7992 0.7718 459 0.8058 0.7992 0.7919 0.7679 0.7716 0.7771 0.7717 229 0.8024 0.8243 0.8053 0.7638 0.7817 0.7727 0.7726 153 0.8153 0.8511 0.8222 0.7647 0.8136 0.7728 0.7759 100 0.8498 0.8922 0.8584 0.7733 0.9007 0.7817 0.7852 50 0.8848 0.9108 0.8903 0.7847 0.9464 0.7931 0.7979 30 0.9689 0.9495 0.9595 0.8277 1.0455 0.8355 0.8498 10 1.0341 1.0406 1.0665 0.9596 1.1150 0.9492 0.9449 1 R(-1.0, 1.0) Constant(1.0) R(0.5, 1.0) wPCC PCC Somers’ d Co Rated Neighborhood
  21. 21. a) our error measures are not good enough?
  22. 22. a) our error measures are not good enough? b) is there something wrong with the dataset?
  23. 23. a) our error measures are not good enough? b) is there something wrong with the dataset? c) current user-similarity is not strong enough to capture the best recommender relationships? [NO TRUST]
  24. 24. trust enables interaction in environments that are characterised by uncertainty read more: N. Lathia “Learning to Trust on the Move” International Workshop on Trust in Mobile Environments (TIME). IFIPTM, June 2008, Trondheim, Norway.
  25. 25. filtering is a trust problem weight users based on how like-minded you have been to them in the past (similarity) to: weight users based on the quality of the opinions you receive from them (trust)
  26. 26. 1. find neighbours 2. make predictions 4. get feedback 3. recommend trust engine risk engine source: N. Lathia, S. Hailes, L. Capra “Trust-Based Collaborative Filtering” Joint iTrust and PST Conference (IFIPTM), June 2008, Trondheim, Norway.
  27. 27. (trust-based) select valuable neighbours
  28. 28. (trust-based) select valuable neighbours
  29. 29. (trust-based) select valuable neighbours a-symmetric value-added reward information
  30. 30. <ul><li>(movielens movie-rating data example) </li></ul>me you 26 18 4 0 0 5* 30 26 2 3 4* 19 10 1 1 4* 5 13 1 0 3* 2 11 2 0 2* 1 5 2 1 1* 5* 3* 2* 1*
  31. 31. <ul><li>(movielens movie-rating data example) </li></ul>me you 26 18 4 0 0 5* 30 26 2 3 4* 19 10 1 1 4* 5 13 1 0 3* 2 11 2 0 2* 1 5 2 1 1* 5* 3* 2* 1*
  32. 32. <ul><li>can we learn to interpret neighbour recommendations? </li></ul>me you 26 18 4 0 0 5* 30 26 2 3 4* 19 10 1 1 4* 5 13 1 0 3* 2 11 2 0 2* 1 5 2 1 1* 5* 3* 2* 1*
  33. 33. <ul><li>“ do what I mean , not what I say ” </li></ul>semantic distance (rating transpose)
  34. 34. no profile? trust bootstrapping [see: Massa et al] uncertain? award potential recommenders extensions? Note (a): a [computed] subset of the community of users seems to be good recommenders for everybody. analysis?
  35. 35. a user kNN algorithm generates a graph source: N. Lathia, S. Hailes, L. Capra “kNN CF: A Temporal Social Network” To appear, ACM RecSys ’08. October 2008, Lausanne, Switzerland
  36. 36. what properties does the graph have? <ul><li>growth </li></ul><ul><li>preferential attachment </li></ul><ul><li>converging neighbourhoods </li></ul><ul><li>very short diameter </li></ul><ul><li>low reciprocity </li></ul><ul><li>fixed out-degree </li></ul><ul><li>power-law in-degree </li></ul><ul><li>similarity-dependent evolution </li></ul>
  37. 37. similarity evolution
  38. 38. similarity evolution
  39. 39. what properties does the graph have? <ul><li>growth </li></ul><ul><li>preferential attachment </li></ul><ul><li>converging neighbourhoods </li></ul><ul><li>very short diameter </li></ul><ul><li>low reciprocity </li></ul><ul><li>fixed out-degree </li></ul><ul><li>power-law in-degree </li></ul><ul><li>similarity-dependent evolution </li></ul>
  40. 40. Note (b): a [kNN structure] subset of the community of users seems to be good recommenders for everybody.
  41. 41. <ul><li>Exploring Recommender Systems: </li></ul>the wisdom of the few
  42. 42. users items sparse ratings situation:
  43. 43. users items sparse ratings accuracy coverage uncertainty privacy noise complication:
  44. 44. users items sparse ratings more information one solution: (difficult)
  45. 45. users items sparse ratings more information our solution:
  46. 46. users items sparse ratings our solution: experts: rotten tomatoes flixster dense ratings items
  47. 47. training set items sparse ratings source set dense ratings test set
  48. 48. different characteristics (data)…
  49. 49. different characteristics (users)…
  50. 50. changing collaborative filtering before: find best method data users ?
  51. 51. changing collaborative filtering now: find best data source ? user-dependent problem can provably generate an RMSE < 0.856! Goal: adaptive collaborative filtering
  52. 52. <ul><li>Exploring Recommender Systems: </li></ul>trust, graphs, and experts neal lathia [email_address] http://www.cs.ucl.ac.uk/staff/n.lathia

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