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Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
Temporal Diversity in RecSys - SIGIR2010
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Temporal Diversity in RecSys - SIGIR2010

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Paper here: http://www.cs.ucl.ac.uk/staff/n.lathia/publications/sigir10.html

Paper here: http://www.cs.ucl.ac.uk/staff/n.lathia/publications/sigir10.html

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  • 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. recommender systems ● many examples over different web domains ● a lot of research: accuracy ● multiple dimensions of usage that equate to user satisfaction
  • 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. 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. 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. is diversity important?
  • 7. data perspective: growth & activity
  • 8. demographics (in paper): ~104 respondents
  • 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. overview of the surveys
  • 11. Survey 3: Random Movies W1 W2 W3 W4 W5
  • 12. Survey 3: Random Movies W1 W2 W3 W4 W5
  • 13. Survey 2: Popular Movies, Change Each Week W1 W2 W3 W4 W5
  • 14. Survey 2: Popular Movies, Change Each Week W1 W2 W3 W4 W5
  • 15. Survey 1: Popular Movies – No Change W1 W2 W3 W4 W5
  • 16. Closing Questions
  • 17. Closing Questions surprise, unrest, rude compliments, “spot on” 74% important / very important 23% neutral 86% important / very important 95% important / very important
  • 18. how did this affect the way people rated?
  • 19. how did this affect the way people rated?
  • 20. how did this affect the way people rated? S3 Random: Always Bad
  • 21. how did this affect the way people rated? S2 Popular: Quite Good S3 Random: Always Bad
  • 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. is diversity important? (yes)
  • 24. how to measure temporal diversity?
  • 25. measuring temporal diversity diversity = ?
  • 26. measuring temporal diversity diversity = 3/10
  • 27. how much temporal diversity do state-of-the-art CF algorithms provide?
  • 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. profile size vs. diversity baseline kNN SVD
  • 30. profile size vs. diversity baseline kNN SVD
  • 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. 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. accuracy vs. diversity more diverse kNN SVD baseline more accurate
  • 34. how to improve temporal diversity?
  • 35. 3 methods ● temporal switching ● temporal user-based switching ● re-ranking frequent visitor's lists
  • 36. temporal switching ● “jump” between algorithms each week
  • 37. temporal switching ● “jump” between algorithms each week
  • 38. re-ranking visitor's lists ● (like we did in survey 2)
  • 39. re-ranking visitor's lists ● (like we did in survey 2, amazon did in 1998!)
  • 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. 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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