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https://github.com/DyGRec/TGSRec
https://github.com/DyGRec/TGSRec
Last synced: about 7 hours ago
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- Host: GitHub
- URL: https://github.com/DyGRec/TGSRec
- Owner: DyGRec
- Created: 2021-08-22T14:16:48.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2022-07-26T07:18:51.000Z (over 2 years ago)
- Last Synced: 2024-08-02T13:20:02.425Z (3 months ago)
- Language: Python
- Size: 1.54 MB
- Stars: 53
- Watchers: 3
- Forks: 14
- Open Issues: 9
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Metadata Files:
- Readme: README.md
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README
# Introduction
This is the repository of our accepted CIKM 2021 paper "Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer" and the proposed model is TGSRec. Paper is available on [arxiv](https://arxiv.org/abs/2108.06625). This work focuses on multi-steps continuous-time recommendation, where user and item embeddings are generated in any unseen future timestamps. Different from existing sequential recommendation methods, which are optimized for next-item prediction, this work is learned for recommendation in any timestamps.# Update
As we just observed some bugs in existing code, we are rerunning the experiments and will update them to the paper as soon as possible.# Citation
Please cite our paper if using this code.
```
@inproceedings{fan2021continuous,
title={Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer},
author={Fan, Ziwei and Liu, Zhiwei and Zhang, Jiawei and Xiong, Yun and Zheng, Lei and Yu, Philip S.},
booktitle={Proceedings of the 30th ACM International Conference on Information and Knowledge Management},
year={2021},
organization={ACM}
}
```# Implementation
The code is implemented based on [TGAT](https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs).
## Environment Setup
The code is tested under a Linux desktop (w/ GTX 1080 Ti GPU) with Pytorch and Python 3.6.
Create the requirement with the requirements.txt## ML-100K Dataset Execution
### Sample code to run
```
python run_TGREC.py -d ml-100k --uniform --bs 600 --lr 0.001 --n_degree 30 --agg_method attn --attn_mode prod --gpu 0 --n_head 2 --n_layer 2 --prefix Video_Games_bce --node_dim 32 --time_dim 32 --drop_out 0.3 --reg 0.3 --negsampleeval 1000
```