Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System

2025-09-05

AI Summary

To address data sparsity and insufficient interaction modeling in graph-based contrastive learning for recommendation, this paper proposes a hybrid matrix factorization framework integrating low-rank matrix factorization (MF) and singular value decomposition (SVD) to generate augmented views enriched with global collaborative signals—replacing conventional data augmentation strategies reliant on graph structural perturbation or clustering. MF captures high-order collaborative patterns, while SVD extracts latent semantic structures; their complementary modeling of user–item interactions enhances the discriminative power of self-supervised graph neural network training. Extensive experiments on multiple public benchmarks demonstrate that the proposed method significantly outperforms state-of-the-art baselines, particularly under sparse-data conditions, where it achieves substantial gains in recommendation accuracy. These results validate its effectiveness in mitigating data sparsity and improving robustness in collaborative filtering.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Recommender Systems
• Computer Vision: Representation Learning for Vision

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

In recent years, methods that combine contrastive learning with graph neural networks have emerged to address the challenges of recommendation systems, demonstrating powerful performance and playing a significant role in this domain. Contrastive learning primarily tackles the issue of data sparsity by employing data augmentation strategies, effectively alleviating this problem and showing promising results. Although existing research has achieved favorable outcomes, most current graph contrastive learning methods are based on two types of data augmentation strategies: the first involves perturbing the graph structure, such as by randomly adding or removing edges; and the second applies clustering techniques. We believe that the interactive information obtained through these two strategies does not fully capture the user-item interactions. In this paper, we propose a novel method called HMFGCL (Hybrid Matrix Factorization Based Graph Contrastive Learning), which integrates two distinct matrix factorization techniques-low-rank matrix factorization (MF) and singular value decomposition (SVD)-to complementarily acquire global collaborative information, thereby constructing enhanced views. Experimental results on multiple public datasets demonstrate that our model outperforms existing baselines, particularly on small-scale datasets.

Problem

• Addresses data sparsity in recommendation systems
• Enhances user-item interaction capture with hybrid methods
• Improves graph contrastive learning through matrix factorization

Innovation

• Hybrid Matrix Factorization for enhanced views
• Combines low-rank MF and SVD techniques
• Graph contrastive learning with global information