A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model

TL;DR

This paper introduces the NPALF model, which enhances latent factor analysis for high-dimensional incomplete data by integrating nonlinear PID control and PSO-based parameter adaptation, resulting in faster convergence and improved accuracy.

Contribution

It proposes a novel nonlinear PID-enhanced adaptive latent factor model that improves convergence and prediction accuracy over existing methods.

Findings

Outperforms five state-of-the-art LFA models in convergence speed.

Achieves higher prediction accuracy on HDI datasets.

Demonstrates effectiveness across four representative datasets.

Abstract

High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model suffers from slow convergence since it only considers the current learning error. To address this critical issue, this paper proposes a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1) rebuilding the learning error via considering the past learning errors following the principle of a nonlinear PID controller; b) implementing all parameters adaptation effectively following the principle of a particle swarm optimization (PSO) algorithm. Experience results on four representative HDI datasets indicate that compared with five state-of-the-art LFA models, the NPALF model achieves better convergence rate and prediction accuracy for missing data of an HDI data.