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Linear Recurrent Units for Sequential Recommendation

This repository is the PyTorch implementation for WSDM 2024 paper:

Linear Recurrent Units for Sequential Recommendation [Paper][Code] (BibTex citation at the bottom)

Zhenrui Yue*, Yueqi Wang*, Zhankui He†, Huimin Zeng, Julian McAuley, Dong Wang. Linear Recurrent Units for Sequential Recommendation.

<img src=media/overall_model_arch.png width=1000>

Requirements

Numpy, pandas, pytorch etc. For our detailed running environment see requirements.txt

How to run LRURec

The command below specifies the training of LRURec on MovieLens-1M.

python train.py --dataset_code=ml-1m

Excecute the above command (with arguments) to train LRURec, select dataset_code from ml-1m, beauty, video, sports, steam and xlong. XLong must be downloaded separately and put under ./data/xlong for experiments. Once trainin is finished, evaluation is automatically performed with models and results saved in ./experiments.

Performance

The table below reports our main performance results, with best results marked in bold and second best results underlined. For training and evaluation details, please refer to our paper.

<img src=media/performance.png width=1000>

Citation

Please consider citing the following paper if you use our methods in your research:

@inproceedings{yue2024linear,
  title={Linear recurrent units for sequential recommendation},
  author={Yue, Zhenrui and Wang, Yueqi and He, Zhankui and Zeng, Huimin and McAuley, Julian and Wang, Dong},
  booktitle={Proceedings of the 17th ACM International Conference on Web Search and Data Mining},
  pages={930--938},
  year={2024}
}