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Graph Neural Networks in Recommender Systems: A Survey

arXiv 2011.02260 ↗First posted 4 Nov 2020cs.IR cs.LGcited by 493

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Abstract (arXiv)

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.

Key facts. Cited by 493 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2020.

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Citing papers per year: 2021: 15; 2022: 61; 2023: 130; 2024: 129; 2025: 100; 2026: 58.

493 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  3. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  4. 2024GraphRouter: A Graph-based Router for LLM Selectionscited by 154
  5. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 137
  6. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  7. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  8. 2023PRODIGY: Enabling In-context Learning Over Graphscited by 97
  9. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  10. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  11. 2023Data Distillation: A Surveycited by 86
  12. 2023Machine Unlearning: Solutions and Challengescited by 85
38 more
  1. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  2. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
  3. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  4. 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modelingcited by 63
  5. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  6. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  7. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  8. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  9. 2023Demystifying Oversmoothing in Attention-Based Graph Neural Networkscited by 47
  10. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  11. 2022TwiBot-22: Towards Graph-Based Twitter Bot Detectioncited by 44
  12. 2022GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Gamescited by 42
  13. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  14. 2023Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvaturecited by 39
  15. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  16. 2024TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphscited by 35
  17. 2023Mind the Label Shift of Augmentation-based Graph OOD Generalizationcited by 34
  18. 2023VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPscited by 34
  19. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  20. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  21. 2024Graph Transformers: A Surveycited by 32
  22. 2022Unlearning Graph Classifiers with Limited Data Resourcescited by 31
  23. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  24. 2024Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNscited by 31
  25. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  26. 2021Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximizationcited by 28
  27. 2023An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attentioncited by 28
  28. 2022Graph Neural Networks in IoT: A Surveycited by 27
  29. 2023Ensuring User-side Fairness in Dynamic Recommender Systemscited by 27
  30. 2022Model Degradation Hinders Deep Graph Neural Networkscited by 26
  31. 2022A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metricscited by 26
  32. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
  33. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 26
  34. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  35. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 26
  36. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  37. 2023PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Predictioncited by 25
  38. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25

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