Local Collaborative Ranking
Venue
Proceedings of the 23rd International World Wide Web Conference (WWW), ACM (2014)
Publication Year
2014
Authors
Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, Yoram Singer
BibTeX
Abstract
Personalized recommendation systems are used in a wide variety of applications such
as electronic commerce, social networks, web search, and more. Collaborative
filtering approaches 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 \emph{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 approximation based on the Frobenius norm with a
general empirical risk minimization for ranking losses. Our experiments 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.
