  Current state of the art algorithms for recommender systems are mainly based
on collaborative filtering, which exploits user ratings to discover latent
factors in the data. These algorithms unfortunately do not make effective use
of other features, which can help solve two well identified problems of
collaborative filtering: cold start (not enough data is available for new users
or products) and concept shift (the distribution of ratings changes over time).
To address these problems, we propose Modurec: an autoencoder-based method that
combines all available information using the feature-wise modulation mechanism,
which has demonstrated its effectiveness in several fields. While time
information helps mitigate the effects of concept shift, the combination of
user and item features improve prediction performance when little data is
available. We show on Movielens datasets that these modifications produce
state-of-the-art results in most evaluated settings compared with standard
autoencoder-based methods and other collaborative filtering approaches.
