Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging 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, 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 both the diffusion, i.e., low-pass filter, and the reaction, i.e., high-pass filter, equations. Our proposed CL-based training occurs between reaction and diffusion-based embeddings, so there is no need for graph augmentations. Experimental evaluation on 6 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：对比学习作为一种有前景的技术，通过利用原始数据中的自监督信号应对数据稀疏性挑战，已在推荐系统改进中崭露头角。当前研究已探索将对比学习与基于图卷积网络的协同过滤方法相融合。然而，现有基于对比学习的推荐模型高度依赖低通滤波和图增强技术。本文提出一种面向推荐系统的新型对比学习方法——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）与反应方程（即高通滤波）自主设计了适用于协同过滤的图卷积网络。所提出的基于对比学习的训练过程在反应与扩散产生的嵌入表征之间进行，因而无需图增强操作。在六个基准数据集上的实验评估表明，本方法优于当前最先进的基于对比学习的推荐模型。通过提升推荐准确性与多样性，该方法为推荐系统中对比学习的发展带来了实质性突破。