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 original release at huawei-noah/benchmark/FuxiCTR.
π If you find our code or benchmarks helpful 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]
Key Features
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Configurable: Both data preprocessing and models are modularized and configurable.
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Tunable: Models can be automatically tuned with easy configuration.
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Reproducible: All the benchmarks can be easily reproduced.
Model List
- π Check reusable dataset splits for CTR prediction.
- π Check benchmarking configurations and steps.
- π Check BARS benchmark website.
Installation
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 | δΈζζη¨
API Documentation
Check an overview of code structure for details on API design.
Discussion
Welcome to join our WeChat group for any question and discussion.
Join Us
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 jamie.zhu@huawei.com.