A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis

TL;DR

This paper introduces a Dynamic-Neighbor Particle Swarm Optimizer for Latent Factor Analysis, improving search efficiency and accuracy by incorporating neighbor cooperation and dynamic hyper-parameter tuning.

Contribution

It proposes a novel PSO variant with neighbor cooperation and dynamic tuning, enhancing LFA performance on high-dimensional incomplete matrices.

Findings

DHPL achieves higher accuracy than existing PSO-based LFA models.

The dynamic hyper-parameter tuning improves convergence stability.

Neighbor cooperation expands the search area effectively.

Abstract

High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.