the effect of correlation coefficients on communities of recommenders neal lathia, stephen hailes, licia capra
how do we model recommender systems?
a) machine learning user ratings recommendations model-based collaborative filtering
b) collaborative filtering user ratings matrix recommendations memory-based collaborative filtering
how do we think about this?
collaborative filtering :  a network of cooperating users exchanging opinions nodes = users links = weighted according to ...
community view of the recommender system: 0.75 -0.43 0.2 0.57 (a very small example)
or, put another way: good bad good good (the relationships in the community)
the similarity values depend on how you derive similarity
pearson: -0.50 weighted-pearson: -0.05 vector: 0.76 = no  agreement   ratings: [2,3,1,5,3] ratings: [4,1,3,2,3]
pearson: bad weighted-pearson: no similarity vector: good = no  agreement   ratings: [2,3,1,5,3] ratings: [4,1,3,2,3]
so what is the best way to build the recommender system network?
like this? good bad good good
or like this? bad good bad good
or like this? no similarity good good bad
each way will change the  distribution  of values over the network: (let’s look at it on the movielens dataset)
pearson distribution:
other distributions:
a) accuracy: how well we can make predictions about unknown items
what if we did this? (random number) (expect terrible results)
the results are  far from terrible !
b) coverage: what proportion of items we can find useful information about (to make predictions)
before : look for information from the top-k neighbours (expect top-k to do quite well) what if we did this ? look for inf...
the results are  terrible (best coverage when all of community used)
why is all of this happening?
a) our  error measures  are not good enough?
a) is there something wrong with the  dataset ?  … it does have the long-tail
c) is user-similarity  not strong enough  to capture the best recommender relationships in the network?
future: trust-based recommender systems (neal’s phd)
the effect of correlation coefficients on communities of recommenders neal lathia, stephen hailes, licia capra all the det...
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The Effect of Correlation Coefficients on Communities of Recommenders

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The Effect of Correlation Coefficients on Communities of Recommenders

  1. 1. the effect of correlation coefficients on communities of recommenders neal lathia, stephen hailes, licia capra
  2. 2. how do we model recommender systems?
  3. 3. a) machine learning user ratings recommendations model-based collaborative filtering
  4. 4. b) collaborative filtering user ratings matrix recommendations memory-based collaborative filtering
  5. 5. how do we think about this?
  6. 6. collaborative filtering : a network of cooperating users exchanging opinions nodes = users links = weighted according to similarity
  7. 7. community view of the recommender system: 0.75 -0.43 0.2 0.57 (a very small example)
  8. 8. or, put another way: good bad good good (the relationships in the community)
  9. 9. the similarity values depend on how you derive similarity
  10. 10. pearson: -0.50 weighted-pearson: -0.05 vector: 0.76 = no agreement ratings: [2,3,1,5,3] ratings: [4,1,3,2,3]
  11. 11. pearson: bad weighted-pearson: no similarity vector: good = no agreement ratings: [2,3,1,5,3] ratings: [4,1,3,2,3]
  12. 12. so what is the best way to build the recommender system network?
  13. 13. like this? good bad good good
  14. 14. or like this? bad good bad good
  15. 15. or like this? no similarity good good bad
  16. 16. each way will change the distribution of values over the network: (let’s look at it on the movielens dataset)
  17. 17. pearson distribution:
  18. 18. other distributions:
  19. 19. a) accuracy: how well we can make predictions about unknown items
  20. 20. what if we did this? (random number) (expect terrible results)
  21. 21. the results are far from terrible !
  22. 22. b) coverage: what proportion of items we can find useful information about (to make predictions)
  23. 23. before : look for information from the top-k neighbours (expect top-k to do quite well) what if we did this ? look for information from anyone who has rated the item
  24. 24. the results are terrible (best coverage when all of community used)
  25. 25. why is all of this happening?
  26. 26. a) our error measures are not good enough?
  27. 27. a) is there something wrong with the dataset ? … it does have the long-tail
  28. 28. c) is user-similarity not strong enough to capture the best recommender relationships in the network?
  29. 29. future: trust-based recommender systems (neal’s phd)
  30. 30. the effect of correlation coefficients on communities of recommenders neal lathia, stephen hailes, licia capra all the details in the paper…

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