An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis

TL;DR

This paper introduces an Adam-enhanced hierarchical particle swarm optimizer for latent factor analysis, improving hyper-parameter tuning and prediction accuracy in large-scale incomplete matrices.

Contribution

It proposes a novel Adam-enhanced PSO-LFA model that automates hyper-parameter adjustment, enhancing efficiency and accuracy over traditional PSO-LFA methods.

Findings

Achieves higher prediction accuracy on real datasets.

Automates hyper-parameter tuning with Adam-enhanced PSO.

Outperforms existing PSO-LFA models in experiments.

Abstract

Digging out the latent information from large-scale incomplete matrices is a key issue with challenges. The Latent Factor Analysis (LFA) model has been investigated in depth to an alyze the latent information. Recently, Swarm Intelligence-related LFA models have been proposed and adopted widely to improve the optimization process of LFA with high efficiency, i.e., the Particle Swarm Optimization (PSO)-LFA model. However, the hyper-parameters of the PSO-LFA model have to tune manually, which is inconvenient for widely adoption and limits the learning rate as a fixed value. To address this issue, we propose an Adam-enhanced Hierarchical PSO-LFA model, which refines the latent factors with a sequential Adam-adjusting hyper-parameters PSO algorithm. First, we design the Adam incremental vector for a particle and construct the Adam-enhanced evolution process for particles. Second, we refine all the latent factors of the target matrix sequentially with our proposed Adam-enhanced PSO's process. The experimental results on four real datasets demonstrate that our proposed model achieves higher prediction accuracy with its peers.