Sparse-Interest Network for Seqeuntial Recommendation

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

Prerequisites

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

Getting Started

Installation

• 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.

Dataset

• 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.

Training

Training on the existing datasets

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.

Training on your own datasets

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>.

Acknowledgement

The structure of our code is based on MIMN.

Cite

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}
}