  Recommender systems assist users in navigating complex information spaces and
focus their attention on the content most relevant to their needs. Often these
systems rely on user activity or descriptions of the content. Social annotation
systems, in which users collaboratively assign tags to items, provide another
means to capture information about users and items. Each of these data sources
provides unique benefits, capturing different relationships.
  In this paper, we propose leveraging multiple sources of data: ratings data
as users report their affinity toward an item, tagging data as users assign
annotations to items, and item data collected from an online database. Taken
together, these datasets provide the opportunity to learn rich distributed
representations by exploiting recent advances in neural network architectures.
We first produce representations that subjectively capture interesting
relationships among the data. We then empirically evaluate the utility of the
representations to predict a user's rating on an item and show that it
outperforms more traditional representations. Finally, we demonstrate that
traditional representations can be combined with representations trained
through a neural network to achieve even better results.
