A fast distributed stochastic Gradient Descent algorithm for matrix factorization

Abstract

The accuracy and effectiveness of matrix factorization technique were well demonstrated in the Netflix movie recommendation contest. Among the numerous solutions for matrix factorization, Stochastic Gradient Descent (SGD) is one of the most widely used algo-rithms. However, as a sequential approach, SGD algorithm cannot directly be used in the Distributed Cluster Environment (DCE). In this paper, we propose a fast distributed SGD algorithm named FDSGD for matrix factorization, which can run efficiently in DCE. This algorithm solves data sharing problem based on independent storage system to avoid data synchronization which may cause a big influence to algorithm performance, and syn-chronous operation problem in DCE using a distributed synchronization tool so that dis-tributed cooperation threads can perform in a harmonious environment.