A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis
This work addresses a specific optimization bottleneck in latent factor analysis for high-dimensional incomplete matrices, representing an incremental improvement over prior methods.
The paper tackles the sub-optimum issue in Particle Swarm Optimization for Latent Factor Analysis by proposing a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA model, which achieves higher accuracy without hyper-parameter tuning compared to existing PSO-incorporated LFA models on two benchmark datasets.
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.