Model code for paper CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems. You can find the paper in https://arxiv.org/abs/2208.02971.
Accepted to CIKM 2022!
The core_code.py file provides a simple code template to show how to deploy CROLoss in your retrieval model (to replace the softmax cross-entropy loss). We hope this can help you better understand our method.
- Python 3
- TensorFlow-GPU >= 1.8 (< 2.0)
- Faiss-GPU
Two preprocessed datasets can be downloaded through:
Dropbox: https://www.dropbox.com/s/m41kahhhx0a5z0u/data.tar.gz
Tsinghua Cloud: https://cloud.tsinghua.edu.cn/f/e5c4211255bc40cba828
You can also download the original datasets and preprocess them by yourself. Follow the tutorial in the link.
You can use python src/train.py --dataset {dataset_name} --kernel_type {kernel_type} --weight_type {weight_type} to train a specific model on a dataset. Other hyperparameters can be found in the code.
For example, you can use python src/train.py --dataset book --kernel_type sigmoid --weight_type even to train CROLoss with sigmoid kernel and even weight on Book dataset.
It should be pointed out that for the choice of hyperparameter weight_type, even means that alpha is equal to 1.0, head12 means that alpha is equal to 1.2, tail08 means that alpha is equal to 0.8, and so on.
To use the Lambda methad, just add lambda prefix for weight_type, such as lambdaeven.
The structure of our code is based on ComiRec.
@inproceedings{tang2022croloss, title={CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems}, author={Tang, Yongxiang and Bai, Wentao and Li, Guilin and Liu, Xialong and Zhang, Yu}, booktitle={Proceedings of the 31st ACM International Conference on Information & Knowledge Management}, pages={1916--1924}, year={2022} }