Numerical Linear Algebra – singular value decomposition.

Cross-domain recommender systems aim to generate or enhance personalized recommendations in a target domain by exploiting knowledge (mainly user preferences) from other source domains. Due to the heterogeneity of item characteristics across domains, content-based recommendation methods are difficult to apply, and collaborative filtering has become the most popular approach to cross-domain recommendation. Nonetheless, recent work has shown that the accuracy of cross-domain collaborative filtering based on matrix factorization can be improved by means of content information; in particular, social tags shared between domains. In this paper, we review state of the art approaches in this direction, and present an alternative recommendation model based on a novel extension of the SVD++ algorithm. Our approach introduces a new set of latent variables, and enriches both user and item profiles with independent sets of tag factors, better capturing the effects of tags on ratings. Evaluating the proposed model in the movies and books domains, we show that it can generate more accurate recommendations than existing approaches, even in cold-start situations