Original implementation for paper Sparse-Interest Network for Sequential Recommendation.

Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu

Accepted to WSDM 2021

• Python 3.6
• TensorFlow-GPU == 1.15.0rc1
• Faiss-GPU == 1.6.4

• Install TensorFlow-GPU 1.15.0rc1

• Install Faiss-GPU based on the instructions here: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md

• Clone this repo git clone https://github.com/Qiaoyut/SINE.git.

• Original links of datasets are:

  • https://grouplens.org/datasets/movielens/
  • http://jmcauley.ucsd.edu/data/amazon/index.html
  • https://tianchi.aliyun.com/dataset/dataDetail?dataId=649

• Two preprocessed datasets (MovieLens and Taobao) are included.

You can use python main.py --dataset {dataset_name} to train SINE on a dataset. Other hyperparameters can be found in the code.

For example, you can use python main.py --dataset ml1m to train SINE model on movieLens dataset.

If you want to train models on your own dataset, you should prepare the following three files:

• train/valid/test file: Each line represents an interaction, which contains three numbers <user_id>,<item_id>,<time_stamp>.

The structure of our code is based on MIMN.

Please cite our paper if you find this code useful for your research:

@inproceedings{tan2021sparse,
  title={Sparse-interest network for sequential recommendation},
  author={Tan, Qiaoyu and Zhang, Jianwei and Yao, Jiangchao and Liu, Ninghao and Zhou, Jingren and Yang, Hongxia and Hu, Xia},
  booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages={598--606},
  year={2021}
}