neal lathia

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

collaborative filtering

statistics

statistics user modeling

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

similarity
trust
user modeling
reputation

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

SIGIR '09

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

experts? Accuracy
Top-N Precision
User Study

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

neighbors?
experts? user
enthusiasts?

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

a problem:
users are subjective, accuracy varies with source

a problem:
users are subjective, accuracy varies with source

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

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

a question:
how to classify users to source set?

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

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

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

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

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

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

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