Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices

TL;DR

This paper introduces a graph-incorporated latent factor analysis model that leverages hidden graph structures to improve representation learning on high-dimensional, sparse matrices common in big data applications.

Contribution

The paper proposes a novel GLFA model that constructs graphs to capture high-order interactions and integrates them into a recurrent LFA framework, enhancing accuracy over existing methods.

Findings

GLFA outperforms six state-of-the-art models in missing data prediction.

Experimental results on three real-world datasets demonstrate its superior representation learning.

The model effectively captures hidden high-order interactions in HiDS matrices.

Abstract

A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform highly accurate representation learning on it is of great significance owing to the great desire of extracting latent knowledge and patterns from it. Latent factor analysis (LFA), which represents an HiDS matrix by learning the low-rank embeddings based on its observed entries only, is one of the most effective and efficient approaches to this issue. However, most existing LFA-based models perform such embeddings on a HiDS matrix directly without exploiting its hidden graph structures, thereby resulting in accuracy loss. To address this issue, this paper proposes a graph-incorporated latent factor analysis (GLFA) model. It adopts two-fold ideas: 1) a graph is constructed for identifying the hidden high-order interaction (HOI) among nodes described by an HiDS matrix, and 2) a recurrent LFA structure is carefully designed with the incorporation of HOI, thereby improving the representa-tion learning ability of a resultant model. Experimental results on three real-world datasets demonstrate that GLFA outperforms six state-of-the-art models in predicting the missing data of an HiDS matrix, which evidently supports its strong representation learning ability to HiDS data.