Recommending content using side information

Collaborative Filtering methods predict user interests and make recommendations just by using the rating matrix. However, in practice there is extensive side information about users and items, such as the age of the user, the actors in a movie, or the abstract of a journal article. In this paper, a novel model called Collaborative Poisson Factorization with Side-information (CPFS) is proposed which extends CTPF by incorporating richer kinds of side information conditionally as a prior to the model. CPFS is a monolithic hybridization model that combines features from different data sources into a single recommendation algorithm. We develop a Gibbs sampler and also a Variational method with closed-form updates for the inference of CPFS and demonstrate its applicability on a range of datasets including movies, books, academic papers, and travel. The extension improves prediction quality, especially in the cold start scenario. The connections between side information and topics are also intuitive.