https://github.com/DyGRec/TGSRec

https://github.com/DyGRec/TGSRec

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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
• 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

```