An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis
This work addresses a hyperparameter tuning problem in optimization for latent factor analysis, offering an incremental improvement for researchers and practitioners in data mining and machine learning.
The paper tackles the issue of manually tuning hyperparameters in Particle Swarm Optimization-based Latent Factor Analysis models, which limits learning rates, by proposing an Adam-enhanced Hierarchical PSO-LFA model that refines latent factors with an Adam-adjusting algorithm, achieving higher prediction accuracy on four real datasets compared to peers.
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.