The document examines the importance of temporal diversity in recommender systems. It defines a metric to measure temporal diversity and analyzes how factors like profile size, ratings added, and time between sessions influence diversity levels. The most accurate collaborative filtering algorithms were not the most diverse over time. The authors propose methods like temporal switching between algorithms and re-ranking recommendations to improve temporal diversity and prevent users from seeing the same recommendations repeatedly.

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

9. demographics (in paper): ~104 respondents

10. 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)
● ....

11. overview of the surveys

12. Survey 3: Random Movies
W1
W2
W3
W4
W5

13. Survey 3: Random Movies
W1
W2
W3
W4
W5

14. Survey 2: Popular Movies, Change Each Week
W1
W2
W3
W4
W5

15. Survey 2: Popular Movies, Change Each Week
W1
W2
W3
W4
W5

16. Survey 1: Popular Movies – No Change
W1
W2
W3
W4
W5

17. Closing Questions

18. Closing Questions
surprise, unrest, rude
compliments, “spot on”
74% important / very important
23% neutral
86% important / very important
95% important / very important

19. how did this affect the way people rated?

20. how did this affect the way people rated?

21. how did this affect the way people rated?
S3 Random: Always Bad

22. how did this affect the way people rated?
S2 Popular: Quite Good
S3 Random: Always Bad

23. 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...

24. is diversity important? (yes)

26. how to measure temporal diversity?

27. measuring temporal diversity
diversity = ?

28. measuring temporal diversity
diversity = 3/10

29. how much temporal diversity do state-of-the-art
CF algorithms provide?

30. 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

31. profile size vs. diversity
baseline kNN SVD

32. profile size vs. diversity
baseline kNN SVD

33. 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

34. 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?

35. accuracy vs. diversity
more diverse
kNN
SVD
baseline
more accurate

36. how to improve temporal diversity?

37. 3 methods
● temporal switching
● temporal user-based switching
● re-ranking frequent visitor's lists

38. temporal switching
● “jump” between algorithms each week

39. temporal switching
● “jump” between algorithms each week

40. re-ranking visitor's lists
● (like we did in survey 2)

41. re-ranking visitor's lists
● (like we did in survey 2, amazon did in 1998!)

42. 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

43. 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
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