An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis

• URL: http://arxiv.org/abs/2302.11956v1
• Date: Thu, 23 Feb 2023 12:10:59 GMT
• Title: An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis
• Authors: Jia Chen, Renyu Zhang, Yuanyi Liu
• Abstract summary: We propose an Adam-enhanced Hierarchical PSO-LFA model, which refines the latent factors with a sequential PSO algorithm. The experimental results on four real datasets demonstrate that our proposed model achieves higher prediction accuracy with its peers.
• Score: 6.960453648000231
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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