  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.
