Speeding Up Distributed Low-Rank Matrix Factorization

Distributed solution for solving low-rank matrix factorization (LMF), an important problem in recommendation system, has recently been studied a lot in order to better deal with the exploding data under the context of Big Data. Stochastic gradient descent is a general technique to solve a large class of convex optimization problems and it is often been chosen to solve problems that deals with large data sets in particular. In this work, we summarize the existing distributed solutions of LMF problem using stochastic gradient descent. We then proposed a novel distributed solution for LMF problem and our solution is able to achieve the best convergence rate as well as fastest execution time when compared with existing solutions. When deployed in the cloud, our solution has the potential to dramatically reduce the cost of complex analytics over massive datasets.