  Collaborative Filtering (CF) is one of the most commonly used recommendation
methods. CF consists in predicting whether, or how much, a user will like (or
dislike) an item by leveraging the knowledge of the user's preferences as well
as that of other users. In practice, users interact and express their opinion
on only a small subset of items, which makes the corresponding user-item rating
matrix very sparse. Such data sparsity yields two main problems for recommender
systems: (1) the lack of data to effectively model users' preferences, and (2)
the lack of data to effectively model item characteristics. However, there are
often many other data sources that are available to a recommender system
provider, which can describe user interests and item characteristics (e.g.,
users' social network, tags associated to items, etc.). These valuable data
sources may supply useful information to enhance a recommendation system in
modeling users' preferences and item characteristics more accurately and thus,
hopefully, to make recommenders more precise. For various reasons, these data
sources may be managed by clusters of different data centers, thus requiring
the development of distributed solutions. In this paper, we propose a new
distributed collaborative filtering algorithm, which exploits and combines
multiple and diverse data sources to improve recommendation quality. Our
experimental evaluation using real datasets shows the effectiveness of our
algorithm compared to state-of-the-art recommendation algorithms.
