  Recommender systems leverage both content and user interactions to generate
recommendations that fit users' preferences. The recent surge of interest in
deep learning presents new opportunities for exploiting these two sources of
information. To recommend items we propose to first learn a user-independent
high-dimensional semantic space in which items are positioned according to
their substitutability, and then learn a user-specific transformation function
to transform this space into a ranking according to the user's past
preferences. An advantage of the proposed architecture is that it can be used
to effectively recommend items using either content that describes the items or
user-item ratings. We show that this approach significantly outperforms
state-of-the-art recommender systems on the MovieLens 1M dataset.
