Awesome
Controllable Multi-Interest Framework for Recommendation
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!
Prerequisites
- Python 3
- TensorFlow-GPU >= 1.8 (< 2.0)
- Faiss-GPU
Getting Started
Installation
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Install TensorFlow-GPU 1.x
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Install Faiss-GPU based on the instructions here: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md
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Clone this repo
git clone https://github.com/THUDM/ComiRec.
Dataset
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Original links of datasets are:
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Two preprocessed datasets can be downloaded through:
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You can also download the original datasets and preprocess them by yourself. You can run
python preprocess/data.py {dataset_name}andpython preprocess/category.py {dataset_name}to preprocess the datasets.
Training
Training on the existing datasets
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.
Training on your own datasets
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..
Common Issues
<details> <summary> The computation of NDCG score. </summary> <br/> I'm so sorry that the computation of NDCG score in the original version (now in `paper` branch) is not consistent with the definition in the paper, as mentioned in the issue #6. I have updated the computation of NDCG score in the `master` branch according to the correct definition. For reproducing the NDCG scores reported in the paper, please use the `paper` branch. By the way, I personally recommend to use the reported results of recall and hit rate only. </details>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.
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{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},
}