LLORMA: Local Low Rank Matrix Approximation
Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is low rank. In this paper, we propose, analyze, and experiment with two procedures, one parallel and the other global, for constructing local matrix approximations. The two approaches approximate the observed matrix as a weighted sum of low rank matrices. These matrices are limited to a local region of the observed matrix. We analyze the accuracy of the proposed local low rank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.
J. Mach. Learn. Res.