RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
This is an incremental improvement for recommender systems, enhancing accuracy and diversity by integrating reaction-diffusion equations into contrastive learning.
The paper tackled the problem of data sparsity in recommender systems by proposing a reaction-diffusion graph contrastive learning model (RDGCL) that eliminates the need for graph augmentations, and it outperformed state-of-the-art CL-based models on 5 benchmark datasets.
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.