Bandits Warm-up Cold Recommender Systems

Understand

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.