用混合矩阵分解增强图对比学习，提升推荐系统性能
Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System
- 融合低秩分解与奇异值分解，互补获取全局协同信息
- 在多个数据集上优于现有基线，小规模数据表现更优
- 适合关注推荐系统中稀疏性问题的开发者和研究者
近年来，结合对比学习与图神经网络的方法在推荐系统中崭露头角，展现出强大性能。对比学习通过数据增强缓解数据稀疏问题，效果显著。现有方法主要依赖两类增强策略：一是随机增删边等图结构扰动；二是聚类技术。我们认为这两种方式未能充分捕捉用户-物品交互信息。本文提出新型方法HMFGCL（基于混合矩阵分解的图对比学习），融合低秩矩阵分解（MF）与奇异值分解（SVD），互补提取全局协同信号，构建更优视图。在多个公开数据集上的实验表明，本模型优于现有基线，尤其在小规模数据集上表现突出。
原文摘要 · Abstract (English)
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.
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