Temporal Diversity in RecSys - SIGIR2010

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Temporal Diversity in RecSys - SIGIR2010

  1. 1. Temporal Diversity in Recommender Systems Neal Lathia1, Stephen Hailes1, Licia Capra1, Xavier Amatriain2 1 Dept. Computer Science, University College London 2 Telefonica Research, Barcelona ACM SIGIR 2010, Geneva n.lathia@cs.ucl.ac.uk @neal_lathia, @xamat EU i-Tour Project
  2. 2. recommender systems ● many examples over different web domains ● a lot of research: accuracy ● multiple dimensions of usage that equate to user satisfaction
  3. 3. evaluating collaborative filtering over time ● design a methodology to evaluate recommender systems that are iteratively updated; explore temporal dimension of filtering algorithms1 1 N. Lathia, S. Hailes, L. Capra. Temporal Collaborative Filtering with Adaptive Neighbourhoods. ACM SIGIR 2009, Boston, USA
  4. 4. temporal diversity ● ...is not concerned with diversity of a single set of recommendations (e.g., are you recommended all six star wars movies at once?) ● ...is concerned with the sequence of recommendations that users see (are you recommended the same items every week?)
  5. 5. contributions ● is temporal recommendation diversity important? ● how to measure temporal diversity and novelty? ● how much temporal diversity do state-of-the-art CF algorithms provide? ● how to improve temporal diversity?
  6. 6. is diversity important?
  7. 7. data perspective: growth & activity
  8. 8. demographics (in paper): ~104 respondents
  9. 9. procedure ● claim: recommender system for “popular movies” ● rate week 1's recommendations ● movie titles, links to IMDB, DVD Covers ● (click through buffer screen) ● rate week 2's recommendations ● (click through buffer screen) ● ....
  10. 10. overview of the surveys
  11. 11. Survey 3: Random Movies W1 W2 W3 W4 W5
  12. 12. Survey 3: Random Movies W1 W2 W3 W4 W5
  13. 13. Survey 2: Popular Movies, Change Each Week W1 W2 W3 W4 W5
  14. 14. Survey 2: Popular Movies, Change Each Week W1 W2 W3 W4 W5
  15. 15. Survey 1: Popular Movies – No Change W1 W2 W3 W4 W5
  16. 16. Closing Questions
  17. 17. Closing Questions surprise, unrest, rude compliments, “spot on” 74% important / very important 23% neutral 86% important / very important 95% important / very important
  18. 18. how did this affect the way people rated?
  19. 19. how did this affect the way people rated?
  20. 20. how did this affect the way people rated? S3 Random: Always Bad
  21. 21. how did this affect the way people rated? S2 Popular: Quite Good S3 Random: Always Bad
  22. 22. how did this affect the way people rated? S2 Popular: Quite Good S1 Starts off Quite Good S1 Ends off Bad S3 Random: Always Bad ...ANOVA details in paper...
  23. 23. is diversity important? (yes)
  24. 24. how to measure temporal diversity?
  25. 25. measuring temporal diversity diversity = ?
  26. 26. measuring temporal diversity diversity = 3/10
  27. 27. how much temporal diversity do state-of-the-art CF algorithms provide?
  28. 28. 3 algorithms – 3 influential factors ● baseline – popularity ranking ● item-based kNN ● singular value decomposition ● profile size vs. diversity ● ratings added vs. diversity ● time between sessions vs. diversity
  29. 29. profile size vs. diversity baseline kNN SVD
  30. 30. profile size vs. diversity baseline kNN SVD
  31. 31. main results ● as profile size increases, diversity decreases ● the more ratings added in the current session, the more diversity will be experienced in next session ● more time between sessions leads to more diversity
  32. 32. consequences ● want to avoid from having profiles that are too large ● (conflict #1) want to encourage users to rate as much as possible ● (conflict #2) want users to visit often, but diversity increases if they don't ● how does this relate back to traditional evaluation metrics?
  33. 33. accuracy vs. diversity more diverse kNN SVD baseline more accurate
  34. 34. how to improve temporal diversity?
  35. 35. 3 methods ● temporal switching ● temporal user-based switching ● re-ranking frequent visitor's lists
  36. 36. temporal switching ● “jump” between algorithms each week
  37. 37. temporal switching ● “jump” between algorithms each week
  38. 38. re-ranking visitor's lists ● (like we did in survey 2)
  39. 39. re-ranking visitor's lists ● (like we did in survey 2, amazon did in 1998!)
  40. 40. contributions/summary ● temporal diversity is important ● defined (simple, extendable) metric to measure temporal recommendation diversity ● analysed factors that influence diversity; most accurate algorithm is not the most diverse ● hybrid-switching/re-ranking can improve diversity
  41. 41. Temporal Diversity in Recommender Systems Neal Lathia1, Stephen Hailes1, Licia Capra1, Xavier Amatriain2 1 Dept. Computer Science, University College London 2 Telefonica Research, Barcelona ACM SIGIR 2010, Geneva n.lathia@cs.ucl.ac.uk @neal_lathia, @xamat Support by: EU FP7 i-Tour Grant 234239

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