Original implementation for paper Controllable Multi-Interest Framework for Recommendation.

Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, Jie Tang

Accepted to KDD 2020 ADS Track!

• Python 3
• TensorFlow-GPU >= 1.8 (< 2.0)
• Faiss-GPU

• Install TensorFlow-GPU 1.x

• 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/THUDM/ComiRec.

Two datasets can be downloaded from https://www.dropbox.com/s/m41kahhhx0a5z0u/data.tar.gz?dl=0

You can use python src/train.py --dataset {dataset_name} --model_type {model_name} to train a specific model on a dataset. Other hyperparameters can be found in the code. (If you share the server with others or you want to use the specific GPU(s), you may need to set CUDA_VISIBLE_DEVICES.)

For example, you can use python src/train.py --dataset book --model_type ComiRec-SA to train ComiRec-SA model on Book dataset.

When training a ComiRec-DR model, you should set --learning_rate 0.005.

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

• train/valid/test file: Each line represents an interaction, which contains three numbers <user_id>,<item_id>,<time_stamp>.
• category file (optional): Each line contains two numbers <item_id>,<cate_id> used for computing diversity..

If you have ANY difficulties to get things working in the above steps, feel free to open an issue. You can expect a reply within 24 hours.

The structure of our code is based on MIMN.

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

@inproceedings{cen2020controllable,
  title = {Controllable Multi-Interest Framework for Recommendation},
  author = {Cen, Yukuo and Zhang, Jianwei and Zou, Xu and Zhou, Chang and Yang, Hongxia and Tang, Jie},
  booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year = {2020},
  pages = {2942–2951},
  publisher = {ACM},
}