Large-Scale Matrix Factorization on TPUs

Posted by Harsh Mehta, Software Engineer, Google Research

Matrix factorization is one of the oldest, yet still widely used, techniques for learning how to recommend items such as songs or movies from user ratings. In its basic form, it approximates a large, sparse (i.e., mostly empty) matrix of user-item interactions with a product of two smaller, denser matrices representing learned item and user features. These dense matrices, in turn, can be used to recommend items to a user with which …

datasets distributed systems factorization recommender systems scale tpus