TiSASRec.paddle

A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation.

Introduction

model

论文:Time Interval Aware Self-Attentive Sequential Recommendation

Results

Datasets Metrics Paper’s Ours abs. improv.
MovieLens-1m [email protected] 0.8038 0.8050 0.0012
MovieLens-1m [email protected] 0.5706 0.5752 0.0046

Requirement

  • Python >= 3
  • PaddlePaddle >= 2.0.0
  • see requirements.txt

Dataset

MovieLens-1m (max_len = 50)

Usage

Train

bash ./script/train.sh

模型在 200 epochs 左右收敛,日志见 nohup.out

Test

bash ./script/eval.sh

可以得到如下结果:

result

References

@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}
}

GitHub

View Github