1. “the wisdom of the few”
neal lathia
xavier amatriain, josep m. pujol, haewoon kwak, nuria oliver

3. tags: internet group
scalable
p2p advanced
social networks
delay-tolerant
performance
wireless
applications systems
content-distribution
pablo rodriguez, niko laoutaris, alberto lopez, josep m.
pujol, domenico giustiniano, georgios siganos, xiao yang
http://research.tid.es/internet/

4. tags: multimedia group
mobility
search hci
recommender systems
context-awareness mobile apps
multi-modal interfaces
social networks activity recognition
emotion
user modelling
nuria oliver, xavier amatriain, joachim neumann, xavier anguera,
mauro cherubini, (jon froehlich, neal lathia, jiejun xu)
http://research.tid.es/multimedia/

5. recommender systems:
“help people find stuff”

6. source: O. Celma & P. Lamere “Music Recommendation Tutorial” ISMIR 2007

7. (one way is to use)
how? nearest neighbours

8. similarity-weighted average of neighbour ratings
(matrix perspective)
items
users

9. similarity-weighted average of neighbour ratings
(matrix perspective)
items
users

10. similarity-weighted average of neighbour ratings
(matrix perspective)
items
users x
x
x

11. items
users
kNN suffers from (a number of) weaknesses!

12. items
users
scalability
kNN suffers from (a number of) weaknesses!

13. items
users
sparsity
scalability
kNN suffers from (a number of) weaknesses!

14. items
users
noise & data quality
sparsity
scalability
kNN suffers from (a number of) weaknesses!

15. what to do?
items
users get more data!

16. what to do?
items
users? (hard)
users
the web? (how?)

17. what to do?
items
rottentomatoes.com
users
netflixprize.com
flixster.com

18. how do they compare?
items
users smaller, denser,
different std. dev, means

19. cross-dataset nearest-neighbours
items
users “crowds” “experts”

20. cross-dataset nearest-neighbours
items
users

21. cross-dataset nearest-neighbours
items
x
x
users x
x
x

22. cross-dataset nearest-neighbours
items weighted cosine similarity
x
x
users x
x
x
pick experts with sim > x
introduce a confidence metric

23. does it work?
“help people find stuff”
prediction accuracy

24. parameters

25. compared to neighbours

26. does it work?
“help people find stuff”
prediction accuracy
recommendation precision
user study

27. A classifier generates a
list of recommendations:

28. A classifier generates a
list of recommendations:
TP
P =
TP+FP
True Positive (TP):
Prediction > r, Rating > r
False Positive (FP):
Prediction > r, Rating < r

29. A classifier generates a
list of recommendations:

30. does it work?
“help people find stuff”
prediction accuracy
recommendation precision
user study

31. (one way is to use)
movies i like..

32. (one way is to use)
movies i don't like..

33. future: multi-source?

34. multi-source prediction
predict

35. multi-source prediction
best source?

36. multi-source prediction
user-dependent: naïve predictors can
perform extremely well if users are
paired with correct source
(data quality is important!)

37. “the wisdom of the few”
neal lathia
xavier amatriain, josep m. pujol, haewoon kwak, nuria oliver