1 research outputs found
Distributed Matrix Factorization using Asynchrounous Communication
Using the matrix factorization technique in machine learning is very common
mainly in areas like recommender systems. Despite its high prediction accuracy
and its ability to avoid over-fitting of the data, the Bayesian Probabilistic
Matrix Factorization algorithm (BPMF) has not been widely used on large scale
data because of the prohibitive cost. In this paper, we propose a distributed
high-performance parallel implementation of the BPMF using Gibbs sampling on
shared and distributed architectures. We show by using efficient load balancing
using work stealing on a single node, and by using asynchronous communication
in the distributed version we beat state of the art implementations.Comment: arXiv admin note: substantial text overlap with arXiv:1705.0415