# Distributed Matrix Factorization using Asynchrounous Communication

arXiv: https://arxiv.org/abs/1705.10633 · HTML: https://mlsanity.com/paper/1705.10633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2017.

## Built on by (most cited first, 1 of 1)

- [Towards Confidential Computing: A Secure Cloud Architecture for Big Data Analytics and AI](https://mlsanity.com/paper/2305.17761/) (2023)

## Abstract (arXiv)

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.