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.

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.

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}
}