Click-through rate (CTR) prediction is an 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.

• 👉 Check the available dataset splits for CTR prediction.
• 👉 Check the benchmarking configurations and results.
• 👉 Check the BARS-CTR-Prediction 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

1. Run the demo to understand the overall workflow

2. How to use dataset and model config files

3. How to preprocess raw csv data to h5 data

4. How to use h5 data as input

5. How to make configurations?

6. How to tune the model hyper-parameters via grid search

7. How to use sequence features

8. How to use pretrained embeddings

Check an overview of code structure for more details on API design. More are comming.

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].