MobiSys Seminar - Nov 4 2008

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MobiSys Seminar - Nov 4 2008

  1. 1. “the wisdom of the few” neal lathia xavier amatriain, josep m. pujol, haewoon kwak, nuria oliver
  2. 2. tags: internet group scalable p2p advanced social networks delay-tolerant performance wireless applications systems content-distribution pablo rodriguez, niko laoutaris, alberto lopez, josep m. pujol, domenico giustiniano, georgios siganos, xiao yang http://research.tid.es/internet/
  3. 3. tags: multimedia group mobility search hci recommender systems context-awareness mobile apps multi-modal interfaces social networks activity recognition emotion user modelling nuria oliver, xavier amatriain, joachim neumann, xavier anguera, mauro cherubini, (jon froehlich, neal lathia, jiejun xu) http://research.tid.es/multimedia/
  4. 4. recommender systems: “help people find stuff”
  5. 5. source: O. Celma & P. Lamere “Music Recommendation Tutorial” ISMIR 2007
  6. 6. (one way is to use) how? nearest neighbours
  7. 7. similarity-weighted average of neighbour ratings (matrix perspective) items users
  8. 8. similarity-weighted average of neighbour ratings (matrix perspective) items users
  9. 9. similarity-weighted average of neighbour ratings (matrix perspective) items users x x x
  10. 10. items users kNN suffers from (a number of) weaknesses!
  11. 11. items users scalability kNN suffers from (a number of) weaknesses!
  12. 12. items users sparsity scalability kNN suffers from (a number of) weaknesses!
  13. 13. items users noise & data quality sparsity scalability kNN suffers from (a number of) weaknesses!
  14. 14. what to do? items users get more data!
  15. 15. what to do? items users? (hard) users the web? (how?)
  16. 16. what to do? items rottentomatoes.com users netflixprize.com flixster.com
  17. 17. how do they compare? items users smaller, denser, different std. dev, means
  18. 18. cross-dataset nearest-neighbours items users “crowds” “experts”
  19. 19. cross-dataset nearest-neighbours items users
  20. 20. cross-dataset nearest-neighbours items x x users x x x
  21. 21. cross-dataset nearest-neighbours items weighted cosine similarity x x users x x x pick experts with sim > x introduce a confidence metric
  22. 22. does it work? “help people find stuff” prediction accuracy
  23. 23. parameters
  24. 24. compared to neighbours
  25. 25. does it work? “help people find stuff” prediction accuracy recommendation precision user study
  26. 26. A classifier generates a list of recommendations:
  27. 27. A classifier generates a list of recommendations: TP P = TP+FP True Positive (TP): Prediction > r, Rating > r False Positive (FP): Prediction > r, Rating < r
  28. 28. A classifier generates a list of recommendations:
  29. 29. does it work? “help people find stuff” prediction accuracy recommendation precision user study
  30. 30. (one way is to use) movies i like..
  31. 31. (one way is to use) movies i don't like..
  32. 32. future: multi-source?
  33. 33. multi-source prediction predict
  34. 34. multi-source prediction best source?
  35. 35. multi-source prediction user-dependent: naïve predictors can perform extremely well if users are paired with correct source (data quality is important!)
  36. 36. “the wisdom of the few” neal lathia xavier amatriain, josep m. pujol, haewoon kwak, nuria oliver

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