Towards Open Benchmarking for Recommender Systems: https://openbenchmark.github.io

Despite the significant progress made in both research and practice of recommender systems over the past two decades, there is a lack of a widely-recognized benchmarking suite in this field. This not only increases the difficulty in reproducing existing studies, but also incurs inconsistent experimental results among them, which largely limit the practical value and potential impact of research in this field. In this project, we present our initiative project aimed for open benchamrking for recommender systems. The benchmarking project allows anyone to easily follow and contribute, and hopefully drive more solid and reproducible research on recommender systems.

The BARS benchmark currently covers the following two tasks.

• CTR Prediction
• Candidate Item Matching

We welcome any contribution that could help improve the BARS benchmark. Check the start guide on how to contribute.

If you have any questions or feedback about the BARS benchamrk, please start a discussion here, or join our WeChat group.