An adaptive latent factor model via particle swarm optimization

Abstract

Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial applications. Stochastic gradient descent (SGD) is an effective algorithm to build an LF model, yet its convergence rate depends vastly on the learning rate which should be tuned with care. Therefore, automatic selection of an optimal learning rate for an SGD-based LF model is a meaningful issue. To address it, this study incorporates the principle of particle swarm optimization (PSO) into an SGD-based LF model for searching an optimal learning rate automatically. With it, we further propose an adaptive Latent Factor (ALF) model. Empirical studies on four HiDS matrices from real industrial applications indicate that an ALF model obvious outperforms an LF model according to convergence rate, and maintain competitive prediction accuracy for missing data.