Neal Lathia is finishing his PhD at UCL in London and has interned at Telefonica I+D in Barcelona. His research interests include collaborative filtering, statistics, and user modeling. Some of the questions his research aims to address are what data shows and how people make decisions. His work also involves concepts like similarity, trust, reputation, and modeling users as experts, neighbors, or enthusiasts. One study aimed to classify users to different information sources to optimize recommendations. While preliminary attempts at classification were unsuccessful, lessons learned are that recommender systems need to model how people make decisions and that accuracy is possible without parameter tuning by moving from single recommendations to classifying users.

1. neal lathia

2. finishing PhD @ UCL (London)
S. Hailes & L. Capra
intern @ Telefonica I+D (Barcelona)
X. Amatriain & J. M. Pujol

3. collaborative filtering

4. statistics

5. statistics user modeling

6. what does the how do people
data show? make decisions?

7. similarity
trust
user modeling
reputation

8. like-
minded?
similarity
friends?
trust
user modeling experts?
reputation

9. SIGIR '09

10. 1. Get “expert” data
2. Compare experts and Netflix
“users”
SIGIR '09
3. Recommend to users based on
experts
4. Evaluate recommendations

11. experts? Accuracy
Top-N Precision
User Study

12. experts? Accuracy
neighbors?
Top-N Precision
User Study
enthusiasts?

13. neighbors?
experts? user
enthusiasts?

14. given:
a simple, un-tuned, kNN predictor and multiple
information sources

15. a problem:
users are subjective, accuracy varies with source

16. a problem:
users are subjective, accuracy varies with source

17. a promise:
optimal classification of users to best source produces
incredibly accurate predictions

18. a promise:
optimal classification of users to best source produces
incredibly accurate predictions

19. a question:
how to classify users to source set?

20. preliminary attempts:
(supervised/unsupervised) kNN-voting, similarity-based,
best-fit, decision trees, SVD, linear combinations,
...unsuccessful

21. preliminary attempts:
learning on the features of user profiles (mean, sd,
what was rated..)
...unsuccessful

22. metrics:
is the overall RMSE improving?
is the precision/recall of the classification improving?

23. lessons:
(1) the web is a goldmine of ratings –
waiting to be harvested
(2) recommender systems need to
model how people make decisions
(3) accuracy is possible without
tuning

24. lessons:
(2) recommender systems need to
model how people make decisions
(3) accuracy is possible without
tuning

25. lessons:
(3) accuracy is possible without
tuning:
...from rating prediction to user classification
...from hybrid predictors to hybrid datasets

26. thanks
n.lathia@cs.ucl.ac.uk