mlsanityData to 7 Oct 2026

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

arXiv 1806.01973 ↗First posted 6 Jun 2018cs.IR cs.LG stat.MLcited by 1,133

Not on the map. Why?

Abstract (arXiv)

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. We deploy PinSage at Pinterest and train it on 7.5 billion examples on a graph with 3 billion nodes representing pins and boards, and 18 billion edges. According to offline metrics, user studies and A/B tests, PinSage generates higher-quality recommendations than comparable deep learning and graph-based alternatives. To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.

Key facts. Cited by 1,133 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 230; 2022: 251; 2023: 207; 2024: 200; 2025: 153; 2026: 92.

1,133 papers.

Sort:

By area:

  1. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  2. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  3. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 246
  4. 2021Identity-aware Graph Neural Networkscited by 197
  5. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  6. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  7. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  8. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  9. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  10. 2021Graph Condensation for Graph Neural Networkscited by 150
  11. 2021GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graphcited by 143
  12. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
38 more
  1. 2023User Behavior Simulation with Large Language Model based Agentscited by 136
  2. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  3. 2021Graph Unlearningcited by 129
  4. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  5. 2022Graph-Coupled Oscillator Networkscited by 123
  6. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  7. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  8. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  9. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  10. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  11. 2021Robustness of Graph Neural Networks at Scalecited by 100
  12. 2021Dirichlet Energy Constrained Learning for Deep Graph Neural Networkscited by 97
  13. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  14. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  15. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  16. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  17. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  18. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  19. 2023Everything is Connected: Graph Neural Networkscited by 85
  20. 2024Position: Graph Foundation Models are Already Herecited by 84
  21. 2021Scaling Up Graph Neural Networks Via Graph Coarseningcited by 80
  22. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  23. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 75
  24. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  25. 2022Recommendation Unlearningcited by 71
  26. 2024Can GNN be Good Adapter for LLMs?cited by 71
  27. 2021Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalizationcited by 70
  28. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  29. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  30. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  31. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  32. 2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
  33. 2021Lorentzian Graph Convolutional Networkscited by 66
  34. 2022A Survey on Graph Representation Learning Methodscited by 66
  35. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 66
  36. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  37. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  38. 2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63

50 most cited of 1,133 All 1,133 (JSON)