update: as expected, with few lines of xavier initialization code added, it converges as fast as original tf version now, pls check github issue of this repo and https://github.com/pmixer/SASRec.pytorch for more details if interested :)

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update: a pretrained model added, pls run the command as below to test its performance:

python main.py --dataset=ml-1m --train_dir=default --dropout_rate=0.2 --device=cuda --state_dict_path='ml-1m_default/TiSASRec.epoch=601.lr=0.001.layer=2.head=1.hidden=50.maxlen=200.pth' --inference_only=true --maxlen=200

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modified based on paper author's tensorflow implementation, switching to PyTorch(v1.6) for simplicity, executable by:

python main.py --dataset=ml-1m --train_dir=default --device=cuda

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

@inproceedings{li2020time,
  title={Time Interval Aware Self-Attention for Sequential Recommendation},
  author={Li, Jiacheng and Wang, Yujie and McAuley, Julian},
  booktitle={Proceedings of the 13th International Conference on Web Search and Data Mining},
  pages={322--330},
  year={2020}
}