Click-through rate (CTR) prediction is a critical task for many industrial applications such as online advertising, recommender systems, and sponsored search. FuxiCTR provides an open-source library for CTR prediction, with key features in configurability, tunability, and reproducibility. We hope this project could benefit both researchers and practitioners with the goal of open benchmarking for CTR prediction tasks.
If you find FuxiCTR useful in your research, please kindly cite the following papers:
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. Open Benchmarking for Click-Through Rate Prediction. The 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021. [Bibtex]
Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang. BARS: Towards Open Benchmarking for Recommender Systems. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022. [Bibtex]
FuxiCTR has the following dependent requirements.
- pytorch 1.10+
- python 3.6+
- pyyaml 5.1+
- scikit-learn
- pandas
- numpy
- h5py
- tqdm
One can easily run each model in the model zoo following the commands below, which is a demo for running DCN. In addition, users can modify the dataset config and model config files to run on their own datasets or on new hyper-parameters.
cd model_zoo/DCN/DCN_torch
python run_expid --expid DCN_test --gpu 0
We have open positions for internships and full-time jobs. If you are interested in research and practice in recommender systems, please send your CV to [email protected].