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.
This repo is the community dev version of the official release at huawei-noah/benchmark/FuxiCTR.
🔔 If you find our code or benchmarks helpful in your research, please kindly cite the following paper.
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]
- 👉 Check available dataset splits for CTR prediction.
- 👉 Check benchmarking configurations and steps.
- 👉 Check BARS-CTR benchmark website.
Please follow the guide for installation. In particular, FuxiCTR has the following dependent requirements.
- python 3.6
- pytorch v1.0/v1.1
- pyyaml >=5.1
- scikit-learn
- pandas
- numpy
- h5py
- tqdm
Tutorials | 中文教程
Check an overview of code structure for details on API design.
Welcome to join our WeChat group for any questions and discussions.
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].