  A standard model for Recommender Systems is the Matrix Completion setting:
given partially known matrix of ratings given by users (rows) to items
(columns), infer the unknown ratings. In the last decades, few attempts where
done to handle that objective with Neural Networks, but recently an
architecture based on Autoencoders proved to be a promising approach. In
current paper, we enhanced that architecture (i) by using a loss function
adapted to input data with missing values, and (ii) by incorporating side
information. The experiments demonstrate that while side information only
slightly improve the test error averaged on all users/items, it has more impact
on cold users/items.

Machine Learning;Information Retrieval