RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 52 of its 87 references that are in the map. First posted on arXiv 27 Dec 2023.
Builds on
52 of 87 references.
References · 52
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
- 2020Graph Contrastive Learning with Augmentationscited by 1,203
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
- 2020Self-supervised Learning: Generative or Contrastivecited by 693
- 2019Neural Graph Collaborative Filteringcited by 660
- 2020Contrastive Multi-View Representation Learning on Graphscited by 651
- 2020A Survey on Contrastive Self-supervised Learningcited by 553
38 more
- 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
- 2020Deep Graph Contrastive Representation Learningcited by 535
- 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
- 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
- 2020Self-supervised Graph Learning for Recommendationcited by 305
- 2019Revisiting Graph Neural Networks: All We Have is Low-Pass Filterscited by 260
- 2021GRAND: Graph Neural Diffusioncited by 258
- 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 194
- 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
- 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
- 2019Graph Neural Ordinary Differential Equationscited by 153
- 2022DiffCSE: Difference-based Contrastive Learning for Sentence Embeddingscited by 131
- 2020Intriguing Properties of Contrastive Lossescited by 129
- 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 119
- 2019Continuous Graph Neural Networkscited by 114
- 2021A Survey on Data Augmentation for Text Classificationcited by 111
- 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
- 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
- 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
- 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
- 2022Hypergraph Contrastive Collaborative Filteringcited by 63
- 2021Dissecting the Diffusion Process in Linear Graph Convolutional Networkscited by 58
- 2022XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendationcited by 57
- 2022GREAD: Graph Neural Reaction-Diffusion Networkscited by 54
- 2021Graph Trend Filtering Networks for Recommendationscited by 53
- 2023Frequency Enhanced Hybrid Attention Network for Sequential Recommendationcited by 52
- 2021How Powerful is Graph Convolution for Recommendation?cited by 50
- 2021Interest-aware Message-Passing GCN for Recommendationcited by 39
- 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
- 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
- 2023An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attentioncited by 28
- 2015On learning optimized reaction diffusion processes for effective image restorationcited by 25
- 2021LT-OCF: Learnable-Time ODE-based Collaborative Filteringcited by 21
- 2021Climate Modeling with Neural Diffusion Equationscited by 20
- 2021Linear, or Non-Linear, That is the Question!cited by 19
- 2023Contrastive Self-supervised Learning in Recommender Systems: A Surveycited by 14
- 2022MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filteringcited by 11
- 2023Graph Convolutions Enrich the Self-Attention in Transformers!cited by 11
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