BMF: Matrix Factorization of Large Scale Data Using Block Based Approach

Abstract

Matrix Factorization on large scale matrices is a memory intensive task. Alternative convergence techniques are needed when the size of the input matrix and the latent feature matrices are higher than the available memory, both on a Central Processing Unit (CPU) as well as a Graphical Processing Unit (GPU). While alternating least squares (ALS) convergence on a CPU could last forever, loading all the required matrices on to a GPU memory may not be possible when the dimensions are significantly high. In this paper, we introduce a novel technique based on dividing the entire data into block matrices and make use of the Stochastic Gradient Descent (SGD) based factorization at the block level.