Dynamic Gaussian Mutation Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis

The latent factor analysis (LF A) is an important and effective matrix decomposition model which has important applications in recommendation system. Traditional LFA models based on the stochastic gradient descent (SGD) algorithm are difficult to adjust the learning rate which is a critical parameter in the model training process. To address this problem, the particle swarm optimization (PSO) algorithm is often used to adjust the learning rate of LFA model due to its powerful global search ability. However, the standard PSO algorithm easily falls into a local optimal solution, causing accuracy loss. In this paper, the dynamic PSO based on Gaussian mutation (DG-PSO) is innovatively proposed by incorporating the elite particle selection strategy to improve its convergence speed and accuracy. Then the DG-PSO based LFA (DGPL) model is constructed by employing the DG- PSO to adaptively tune the learning rate of SGD in the training process. The proposed model is tested on dataset and compared with the state-of-the-art models. Experimental results show that the proposed DGPL is superior than existing LFA models.