SASRec_pytorch:基于 PyTorch 的自注意力序列推荐项目

可用于实现自注意力机制的序列推荐任务，基于 PyTorch 实现，修复位置嵌入等问题，支持训练与推理，能提升 NDCG 和 HR 指标，适合推荐系统研究与应用。【此简介由AI生成】

当前项目代码仓暂无内容

update on 05/23/2025: thx to Wentworth1028 and Tiny-Snow, we have LayerNorm update, for higher NDCG&HR, and here's the doc👍.

update on 04/13/2025: in https://arxiv.org/html/2504.09596v1, I listed the ideas worth to try but not yet due to my limited bandwidth in sparse time.

pls feel free to do these experiments to have fun, and pls consider citing the article if it somehow helps in your recsys exploration:

@article{huang2025revisiting_sasrec,
  title={Revisiting Self-Attentive Sequential Recommendation},
  author={Huang, Zan},
  journal={CoRR},
  volume={abs/2504.09596},
  url={https://arxiv.org/abs/2504.09596},
  eprinttype={arXiv},
  eprint={2504.09596},
  year={2025}
}

or this bib for short

@article{huang2025revisiting,
  title={Revisiting Self-Attentive Sequential Recommendation},
  author={Huang, Zan},
  journal={arXiv preprint arXiv:2504.09596},
  year={2025}
}

paper source code in latex folder.

for questions or collaborations, pls create a new issue in this repo or drop me an email using the email address as shared.

modified based on paper author's tensorflow implementation, switching to PyTorch(v1.6) for simplicity, fixed issues like positional embedding usage etc. (making it harder to overfit, except for that, in recsys, personalization=overfitting sometimes)

code in python folder.

to train:

python main.py --dataset=ml-1m --train_dir=default --maxlen=200 --dropout_rate=0.2 --device=cuda

just inference:

python main.py --device=cuda --dataset=ml-1m --train_dir=default --state_dict_path=[YOUR_CKPT_PATH] --inference_only=true --maxlen=200

output for each run would be slightly random, as negative samples are randomly sampled, here's my output for two consecutive runs:

1st run - test (NDCG@10: 0.5897, HR@10: 0.8190)
2nd run - test (NDCG@10: 0.5918, HR@10: 0.8225)

pls check paper author's repo for detailed intro and more complete README, and here's the paper bib FYI 😃

@inproceedings{kang2018self,
  title={Self-attentive sequential recommendation},
  author={Kang, Wang-Cheng and McAuley, Julian},
  booktitle={2018 IEEE International Conference on Data Mining (ICDM)},
  pages={197--206},
  year={2018},
  organization={IEEE}
}

I see a dozen of citations of the repo🫰, pls use the bib as below if needed.

@misc{Huang_SASRec_pytorch,
author = {Huang, Zan},
title = {{SASRec.pytorch}},
url = {https://github.com/pmixer/SASRec.pytorch},
howpublished = {\url{https://github.com/pmixer/SASRec.pytorch}},
year={2020}
}

项目介绍