1 research outputs found
Bandits Warm-up Cold Recommender Systems
We address the cold start problem in recommendation systems assuming no
contextual information is available neither about users, nor items. We consider
the case in which we only have access to a set of ratings of items by users.
Most of the existing works consider a batch setting, and use cross-validation
to tune parameters. The classical method consists in minimizing the root mean
square error over a training subset of the ratings which provides a
factorization of the matrix of ratings, interpreted as a latent representation
of items and users. Our contribution in this paper is 5-fold. First, we
explicit the issues raised by this kind of batch setting for users or items
with very few ratings. Then, we propose an online setting closer to the actual
use of recommender systems; this setting is inspired by the bandit framework.
The proposed methodology can be used to turn any recommender system dataset
(such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a
strong and insightful link between contextual bandit algorithms and matrix
factorization; this leads us to a new algorithm that tackles the
exploration/exploitation dilemma associated to the cold start problem in a
strikingly new perspective. Finally, experimental evidence confirm that our
algorithm is effective in dealing with the cold start problem on publicly
available datasets. Overall, the goal of this paper is to bridge the gap
between recommender systems based on matrix factorizations and those based on
contextual bandits