Adaptive Regularization-Incorporated Latent Factor Analysis

Abstract

The valuable knowledge contained in High-dimensional and Sparse (HiDS) matrices can be efficiently extracted by a latent factor (LF) model. Regularization techniques are widely incorporated into an LF model to avoid overfitting. The regularization coefficient is very crucial to the prediction accuracy of models. However, its tuning process is time-consuming and boring. This study aims at making the regularization coefficient of a regularized LF model self-adaptive. To do so, an adaptive particle swarm optimization (APSO) algorithm is introduced into a regularized LF model to automatically select the optimal regularization coefficient. Then, to enhance the global search capability of particles, we further propose an APSO and particle swarm optimization (PSO)-incorporated (AP) algorithm, thereby achieving an AP-based LF (APLF) model. Experimental results on four HiDS matrices generated by real applications demonstrate that an APLF model can achieve an automatic selection of regularization coefficient, and is superior to a regularized LF model in terms of prediction accuracy and computational efficiency.