RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916364794462208 |
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| author | Choi, Jeongwhan Wi, Hyowon Lee, Chaejeong Cho, Sung-Bae Lee, Dongha Park, Noseong |
| author_facet | Choi, Jeongwhan Wi, Hyowon Lee, Chaejeong Cho, Sung-Bae Lee, Dongha Park, Noseong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16563 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation Choi, Jeongwhan Wi, Hyowon Lee, Chaejeong Cho, Sung-Bae Lee, Dongha Park, Noseong Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.16563 |