RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation

• URL: http://arxiv.org/abs/2312.16563v2
• Date: Thu, 22 Aug 2024 12:50:09 GMT
• Title: RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
• Authors: Jeongwhan Choi, Hyowon Wi, Chaejeong Lee, Sung-Bae Cho, Dongha Lee, Noseong Park,
• Abstract summary: Contrastive learning (CL) has emerged as a promising technique for improving recommender systems. We propose a novel CL method for recommender systems called the reaction-diffusion graph contrastive learning model (RDGCL) Our proposed CL-based training occurs between reaction and diffusion-based embeddings, so there is no need for graph augmentations.
• Score: 36.33499876095934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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