CDSFM: A Circular Distributed SGLD-Based Factorization Machines

Factorization Machines (FMs) offers attractive performance by combining low-rank data vectors and heuristic features. However, it suffers from the growth of the dataset and the model complexity. Although much efforts have been made to distribute FMs over multiple machines, the computation efficiency is still limited by the foundational master-slave framework. In this paper, we propose CDSFM, which leverages Stochastic Gradient Langevin Dynamics (SGLD) to optimize FMs, and is distributed into a completely new circular framework. Experiments on two genres of datasets show that CDSFM can achieves a 2.3–4.7\(\times \) speed-up over the comparison methods while obtains better performance.