A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data
This work addresses accuracy improvements in latent factor analysis for big data applications, representing an incremental advancement over existing methods.
The paper tackles the suboptimal solution problem in Position-transitional Latent Factor Analysis (PLFA) models for high-dimensional and sparse data by proposing a Sequential-Group-Differential Evolution (SGDE) algorithm to refine latent factors, resulting in a SGDE-PLFA model that outperforms state-of-the-art models on four datasets.
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a Sequential-Group-Differential- Evolution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. As demonstrated by the experiments on four HiDS matrices, a SGDE-PLFA model outperforms the state-of-the-art models.