mlsanityData to 14 Oct 2026

Session-based Recommendation with Graph Neural Networks

arXiv 1811.00855 ↗First posted 1 Nov 2018cs.IR cs.LG stat.MLcited by 312

Not on the map. Why?

Abstract (arXiv)

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graph-structured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

Key facts. Cited by 312 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 58; 2022: 59; 2023: 62; 2024: 61; 2025: 43; 2026: 29.

312 papers.

Sort:

By area:

  1. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  2. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  3. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 121
  4. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  5. 2021STAN: Spatio-Temporal Attention Network for Next Location Recommendationcited by 102
  6. 2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
  7. 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 93
  8. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  9. 2023Data Distillation: A Surveycited by 87
  10. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  11. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 75
  12. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
38 more
  1. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
  2. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  3. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  4. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  5. 2021GraphMI: Extracting Private Graph Data from Graph Neural Networkscited by 50
  6. 2021Automated Self-Supervised Learning for Graphscited by 50
  7. 2021Zero-Shot Recommender Systemscited by 46
  8. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42
  9. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  10. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  11. 2024A Survey of Mambacited by 39
  12. 2022CORE: Simple and Effective Session-based Recommendation within Consistent Representation Spacecited by 37
  13. 2021Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detectioncited by 34
  14. 2021Dynamic Graph Collaborative Filteringcited by 33
  15. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  16. 2022Evidence-aware Fake News Detection with Graph Neural Networkscited by 30
  17. 2021GNN-LM: Language Modeling based on Global Contexts via GNNcited by 27
  18. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26
  19. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  20. 2023Intelligent Model Update Strategy for Sequential Recommendationcited by 25
  21. 2023A Diffusion model for POI recommendationcited by 24
  22. 2022A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applicationscited by 23
  23. 2021Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendationcited by 22
  24. 2022Multi-view Intent Disentangle Graph Networks for Bundle Recommendationcited by 22
  25. 2024IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networkscited by 22
  26. 2021Training Robust Graph Neural Networks with Topology Adaptive Edge Droppingcited by 20
  27. 2022Multi-Behavior Sequential Recommendation with Temporal Graph Transformercited by 20
  28. 2023Embedding in Recommender Systems: A Surveycited by 20
  29. 2024EXGC: Bridging Efficiency and Explainability in Graph Condensationcited by 20
  30. 2024Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Modelscited by 20
  31. 2023GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classificationcited by 19
  32. 2023Graph Masked Autoencoder for Sequential Recommendationcited by 19
  33. 2021Stability of Graph Convolutional Neural Networks to Stochastic Perturbationscited by 18
  34. 2021Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendationcited by 18
  35. 2021Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendationcited by 18
  36. 2022Enhancing Sequential Recommendation with Graph Contrastive Learningcited by 18
  37. 2024Image Fusion for Cross-Domain Sequential Recommendationcited by 17
  38. 2023Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debiascited by 16

50 most cited of 312 All 312 (JSON)