Awesome

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. 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.

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