mlsanityData to 14 Oct 2026

Neural Graph Collaborative Filtering

arXiv 1905.08108 ↗First posted 20 May 2019cs.IR cs.LG cs.SIcited by 661

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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. In this work, we propose to integrate the user-item interactions -- more specifically the bipartite graph structure -- into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/xiangwang1223/neural_graph_collaborative_filtering.

Key facts. Cited by 661 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 May 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 93; 2022: 112; 2023: 139; 2024: 141; 2025: 106; 2026: 70.

661 papers.

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  1. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
  2. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  3. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  4. 2024RelBench: A Benchmark for Deep Learning on Relational Databasescited by 96
  5. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  6. 2022Intent Contrastive Learning for Sequential Recommendationcited by 91
  7. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  8. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  9. 2022Recommendation Unlearningcited by 80
  10. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  11. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  12. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
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  1. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
  2. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  3. 2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63
  4. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  5. 2023GIF: A General Graph Unlearning Strategy via Influence Functioncited by 60
  6. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  7. 2021Contrastive Self-supervised Sequential Recommendation with Robust Augmentationcited by 56
  8. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  9. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  10. 2021How Powerful is Graph Convolution for Recommendation?cited by 50
  11. 2021Knowledge-aware Coupled Graph Neural Network for Social Recommendationcited by 49
  12. 2025RelGNN: Composite Message Passing for Relational Deep Learningcited by 48
  13. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  14. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  15. 2021Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendationcited by 39
  16. 2021Graph Meta Network for Multi-Behavior Recommendationcited by 38
  17. 2022Contrastive Meta Learning with Behavior Multiplicity for Recommendationcited by 37
  18. 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
  19. 2024XRec: Large Language Models for Explainable Recommendationcited by 35
  20. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  21. 2022HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularizationcited by 34
  22. 2021Dynamic Graph Collaborative Filteringcited by 33
  23. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  24. 2021Heterogeneous Graph Representation Learning with Relation Awarenesscited by 31
  25. 2024Linear-Time Graph Neural Networks for Scalable Recommendationscited by 31
  26. 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
  27. 2023Revisiting Link Prediction: A Data Perspectivecited by 29
  28. 2021DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendationcited by 28
  29. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  30. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 28
  31. 2022RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Networkcited by 27
  32. 2022Spiking Graph Convolutional Networkscited by 27
  33. 2022Disentangled Contrastive Learning for Social Recommendationcited by 27
  34. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  35. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  36. 2022Knowledge-enhanced Black-box Attacks for Recommendationscited by 26
  37. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
  38. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26

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