IJCAI Workshop Presentation

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IJCAI Workshop Presentation

  1. 1. neal lathia
  2. 2. finishing PhD @ UCL (London) S. Hailes & L. Capra intern @ Telefonica I+D (Barcelona) X. Amatriain & J. M. Pujol
  3. 3. collaborative filtering
  4. 4. statistics
  5. 5. statistics user modeling
  6. 6. what does the how do people data show? make decisions?
  7. 7. similarity trust user modeling reputation
  8. 8. like- minded? similarity friends? trust user modeling experts? reputation
  9. 9. SIGIR '09
  10. 10. 1. Get “expert” data 2. Compare experts and Netflix “users” SIGIR '09 3. Recommend to users based on experts 4. Evaluate recommendations
  11. 11. experts? Accuracy Top-N Precision User Study
  12. 12. experts? Accuracy neighbors? Top-N Precision User Study enthusiasts?
  13. 13. neighbors? experts? user enthusiasts?
  14. 14. given: a simple, un-tuned, kNN predictor and multiple information sources
  15. 15. a problem: users are subjective, accuracy varies with source
  16. 16. a problem: users are subjective, accuracy varies with source
  17. 17. a promise: optimal classification of users to best source produces incredibly accurate predictions
  18. 18. a promise: optimal classification of users to best source produces incredibly accurate predictions
  19. 19. a question: how to classify users to source set?
  20. 20. preliminary attempts: (supervised/unsupervised) kNN-voting, similarity-based, best-fit, decision trees, SVD, linear combinations, ...unsuccessful
  21. 21. preliminary attempts: learning on the features of user profiles (mean, sd, what was rated..) ...unsuccessful
  22. 22. metrics: is the overall RMSE improving? is the precision/recall of the classification improving?
  23. 23. lessons: (1) the web is a goldmine of ratings – waiting to be harvested (2) recommender systems need to model how people make decisions (3) accuracy is possible without tuning
  24. 24. lessons: (2) recommender systems need to model how people make decisions (3) accuracy is possible without tuning
  25. 25. lessons: (3) accuracy is possible without tuning: ...from rating prediction to user classification ...from hybrid predictors to hybrid datasets
  26. 26. thanks n.lathia@cs.ucl.ac.uk

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