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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
| Date | Stars |
|---|---|
| 2026-07-31 | 1080 |
| 2026-08-06 | 1080 |
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This week
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# GNN based Recommender Systems
An index of recommendation algorithms that are based on Graph Neural Networks.
Our survey **A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions** is accepted by ACM Transactions on Recommender Systems.
A preprint is available on arxiv: [link](https://arxiv.org/pdf/2109.12843v2.pdf)
Please cite our survey paper if this index is helpful.
```
@article{gao2022survey,
title={A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions},
author={Gao, Chen and Zheng, Yu and Li, Nian and Li, Yinfeng and Qin, Yingrong and Piao, Jinghua and Quan, Yuhan and Chang, Jianxin and Jin, Depeng and He, Xiangnan and Li, Yong},
journal={ACM Transactions on Recommender Systems (TORS)},
year={2022}
}
```
```
Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., & Li, Y. (2022). A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Transactions on Recommender Systems (TORS).
```
# Table of Contents
- [GNN in different recommendation stages](#Recommendation-Stages)
- [Matching](#Matching)
- [Ranking](#Ranking)
- [Re-ranking](#Re-ranking)
- [GNN in different recommendation scenarios](#Recommendation-Scenarios)
- [Social Recommendation](#Social-Recommendation)
- [Sequential Recommendation](#Sequential-Recommendation)
- [Session Recommendation](#Session-Recommendation)
- [Bundle Recommendation](#Bundle-Recommendation)
- [Cross Domain Recommendation](#Cross-Domain-Recommendation)
- [GNN for different recommendation objectives](#Recommendation-Objectives)
- [Multi-behavior Recommendation](#Multi-behavior-Recommendation)
- [Diversity](#Diversity)
- [Explainability](#Explainability)
- [Fairness](#Fairness)
## Recommendation Stages
### Matching
| **Name** | **Paper** | **Venue** | **Year** | **Code** |
| --- | --- | --- | --- | --- |
| GCMC | [Berg, R. V. D., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. _arXiv preprint arXiv:1706.02263_.](https://arxiv.org/pdf/1706.02263.pdf) | arxiv | 2017 | [Python](https://paperswithcode.com/paper/graph-convolutional-matrix-completion) |
| Pin-Sage | [Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018, July). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 974-983).](https://arxiv.org/pdf/1806.01973) | KDD | 2018 | [Python](https://paperswithcode.com/paper/graph-convolutional-neural-networks-for-web) |
| NGCF | [Wang, X., He, X., Wang, M., Feng, F., & Chua, T. S. (2019, July). Neural graph collaborative filtering. In _Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval_ (pp. 165-174).](https://arxiv.org/pdf/1905.08108.pdf) | SIGIR | 2019 | [Python](https://paperswithcode.com/paper/neural-graph-collaborative-filtering) |
| LightGCN | [He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020, July). Lightgcn: Simplifying and powering graph convolution network for recommendation. In _Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval_ (pp. 639-648).](https://arxiv.org/pdf/2002.02126.pdf) | SIGIR | 2020 | [Python](https://paperswithcode.com/paper/lightgcn-simplifying-and-powering-graph) |
| NIA-GCN | [Sun, J., Zhang, Y., Guo, W., Guo, H., Tang, R., He, X., ... & Coates, M. (2020, July). Neighbor interaction aware graph convolution networks for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1289-1298).](https://dl.acm.org/doi/abs/10.1145/3397271.3401123) | SIGIR | 2020 | NA |
| DGCF | [Wang, X., Jin, H., Zhang, A., He, X., Xu, T., & Chua, T. S. (2020, July). Disentangled graph collaborativeExcerpt of 41,761 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e0e8bc66e50a3c54, llm:Repository topics and description: gcn, gnn, graph-convolutional-networks, graph-neural-networks, graph-representation-learning, recommendation, recommendation-system; description: 'An index of recommendation algorithms that are based on Graph Neural Networks.'
matched fp:e0e8bc66e50a3c54, llm:Repository topics and description: gcn, gnn, graph-convolutional-networks, graph-neural-networks, graph-representation-learning, recommendation, recommendation-system; description: 'An index of recommendation algorithms that are based on Graph Neural Networks.'