EulerNet

## Requirements ``` tensorflow==2.4.1 python==3.7.3 cudatoolkit==11.3.1 pytorch==1.11.0 ``` ## Dataset Preparation We follow [FmFM](https://github.com/yahoo/FmFM) to process the [Criteo](http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/) and [Avazu](https://www.kaggle.com/c/avazu-ctr-prediction/data) Dataset, and follow [DCNV2](https://dl.acm.org/doi/abs/10.1145/3442381.3450078) to process the [MovieLens-1M](https://drive.google.com/drive/folders/1so0lckI6N6_niVEYaBu-LIcpOdZf99kj?usp=sharing) dataset. The scripts for dataset processing can be found under the /DataSource folder. You first need to download the raw dataset files and put them into the /DataSource folder. Then pre-process the data: ``` python DataSource/[dataset]_parse.py ``` Finally, get the files for training, validation, and testing: ``` python DataSource/split.py ``` ## Training ``` python train.py --config_files=[dataset].yaml ``` ## Cite If you find EulerNet useful for your research or development, please cite the following papers: [EulerNet](https://dl.acm.org/doi/10.1145/3539618.3591681). ```bibtex @inproceedings{tian2023eulernet, title = {EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction}, author = {Tian, Zhen and Bai, Ting and Zhao, Wayne Xin and Wen, Ji-Rong and Cao, Zhao}, booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval}, pages = {1376–1385}, year = {2023}, } ```