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.

我们提出了一种新颖的对比学习方法，叫做反应扩散图对比学习模型 (RDGCL)，用于改进推荐系统的准确度和多样性。我们的方法结合了对比学习、图卷积网络和协同过滤技术，通过设计我们自己的图卷积网络以及扩散和反应方程，在推荐系统中实现了反应和扩散嵌入之间的对比学习，从而不需要图数据增强。在 6 个基准数据集上的实验评估表明，我们的方法优于目前最先进的基于对比学习的推荐模型。