Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from raw data. Integration of CL with graph convolutional network (GCN)-based collaborative filterings (CFs) has been explored in recommender systems. However, current CL-based recommendation models heavily rely on low-pass filters and graph augmentations. In this paper, inspired by the reaction-diffusion equation, we propose a novel CL method for recommender systems called the reaction-diffusion graph contrastive learning model (RDGCL). We design our own GCN for CF based on the equations of diffusion, i.e., low-pass filter, and reaction, i.e., high-pass filter. Our proposed CL-based training occurs between reaction and diffusion-based embeddings, so there is no need for graph augmentations. Experimental evaluation on 5 benchmark datasets demonstrates that our proposed method outperforms state-of-the-art CL-based recommendation models. By enhancing recommendation accuracy and diversity, our method brings an advancement in CL for recommender systems.

翻译：对比学习（CL）已成为改进推荐系统的有效技术，它通过利用原始数据中的自监督信号来应对数据稀疏性的挑战。在推荐系统中，将CL与基于图卷积网络（GCN）的协同过滤（CF）方法相结合已有相关探索。然而，现有的基于CL的推荐模型严重依赖于低通滤波器和图数据增强。本文受反应-扩散方程启发，提出了一种新颖的推荐系统对比学习方法，称为反应-扩散图对比学习模型（RDGCL）。我们基于扩散方程（即低通滤波器）和反应方程（即高通滤波器）设计了用于协同过滤的专用GCN。所提出的基于CL的训练在反应与扩散生成的嵌入表示之间进行，因此无需图数据增强。在五个基准数据集上的实验评估表明，我们提出的方法优于当前最先进的基于CL的推荐模型。通过提升推荐的准确性和多样性，本方法推动了推荐系统中对比学习的发展。