Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/pmixer/tisasrec.pytorch
https://github.com/JiachengLi1995/TiSASRec in PyTorch
https://github.com/pmixer/tisasrec.pytorch
pytorch recommender-system sequential-models tisasrec
Last synced: 7 days ago
JSON representation
https://github.com/JiachengLi1995/TiSASRec in PyTorch
- Host: GitHub
- URL: https://github.com/pmixer/tisasrec.pytorch
- Owner: pmixer
- Created: 2020-09-17T00:17:01.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2020-11-22T03:14:55.000Z (almost 4 years ago)
- Last Synced: 2023-03-05T16:42:55.299Z (over 1 year ago)
- Topics: pytorch, recommender-system, sequential-models, tisasrec
- Language: Python
- Homepage:
- Size: 6.72 MB
- Stars: 57
- Watchers: 1
- Forks: 13
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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 :)
---
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
```---
modified based on [paper author's tensorflow implementation](https://github.com/JiachengLi1995/TiSASRec), 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](https://github.com/JiachengLi1995/TiSASRec) 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}
}
```