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MobiSys Seminar - Nov 4 2008
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MobiSys Seminar - Nov 4 2008

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  • 1. “the wisdom of the few” neal lathia xavier amatriain, josep m. pujol, haewoon kwak, nuria oliver
  • 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. 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. recommender systems: “help people find stuff”
  • 5. source: O. Celma & P. Lamere “Music Recommendation Tutorial” ISMIR 2007
  • 6. (one way is to use) how? nearest neighbours
  • 7. similarity-weighted average of neighbour ratings (matrix perspective) items users
  • 8. similarity-weighted average of neighbour ratings (matrix perspective) items users
  • 9. similarity-weighted average of neighbour ratings (matrix perspective) items users x x x
  • 10. items users kNN suffers from (a number of) weaknesses!
  • 11. items users scalability kNN suffers from (a number of) weaknesses!
  • 12. items users sparsity scalability kNN suffers from (a number of) weaknesses!
  • 13. items users noise & data quality sparsity scalability kNN suffers from (a number of) weaknesses!
  • 14. what to do? items users get more data!
  • 15. what to do? items users? (hard) users the web? (how?)
  • 16. what to do? items rottentomatoes.com users netflixprize.com flixster.com
  • 17. how do they compare? items users smaller, denser, different std. dev, means
  • 18. cross-dataset nearest-neighbours items users “crowds” “experts”
  • 19. cross-dataset nearest-neighbours items users
  • 20. cross-dataset nearest-neighbours items x x users x x x
  • 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. does it work? “help people find stuff” prediction accuracy
  • 23. parameters
  • 24. compared to neighbours
  • 25. does it work? “help people find stuff” prediction accuracy recommendation precision user study
  • 26. A classifier generates a list of recommendations:
  • 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. A classifier generates a list of recommendations:
  • 29. does it work? “help people find stuff” prediction accuracy recommendation precision user study
  • 30. (one way is to use) movies i like..
  • 31. (one way is to use) movies i don't like..
  • 32. future: multi-source?
  • 33. multi-source prediction predict
  • 34. multi-source prediction best source?
  • 35. multi-source prediction user-dependent: naïve predictors can perform extremely well if users are paired with correct source (data quality is important!)
  • 36. “the wisdom of the few” neal lathia xavier amatriain, josep m. pujol, haewoon kwak, nuria oliver