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?
18. Closing Questions
surprise, unrest, rude
compliments, “spot on”
74% important / very important
23% neutral
86% important / very important
95% important / very important
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...
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?
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
Grant 234239