Local collaborative ranking

Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering ap-proaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low-rank. In this paper, we examine an alternative approach in which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank ap-proximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our exper-iments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems. Categories and Subject Descriptors [Information retrieval]: Retrieval tasks and goals—Rec-ommender systems; [Information systems applications]: Data mining—Collaborative filtering; [Machine Learn-ing]: Supervised learning—Rankin