A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data

TL;DR

This paper introduces a novel optimization approach combining differential evolution with latent factor analysis to improve modeling of high-dimensional, sparse data, demonstrating superior performance over existing models.

Contribution

It proposes a Sequential-Group-Differential-Evolution algorithm to refine latent factors in a PLFA model, enhancing accuracy for HiDS matrices.

Findings

SGDE-PLFA outperforms state-of-the-art models on four HiDS matrices.

The method achieves higher accuracy and efficiency in representing complex data.

Experimental results validate the effectiveness of the proposed approach.

Abstract

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.