RBoard统一评测推荐系统，让实验可复现、结果可比。
RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks
- 构建统一平台，覆盖点击率预测、顶N推荐等任务
- 多数据集评测+标准化流程，确保结果可比性
- 开源代码可下载，适合学术与工业界研究复现
推荐系统研究缺乏可复现和算法对比的标准基准。我们提出RBoard，一个全新框架，为多种推荐任务（包括点击率预测、顶N推荐等）提供全面的基准测试平台。其核心目标是实现跨场景的完全可复现与可重用实验。框架在每个任务下对多种数据集评估算法，并聚合结果以进行整体性能分析。通过实施标准化评估协议，确保一致性与可比性。为提升可复现性，所有用户提交的代码均可轻松下载并执行，使研究者能可靠复现成果并在此基础上推进工作。通过提供统一平台，支持各类推荐场景下的严谨、可复现评估，RBoard旨在加速领域进展，并在学术界与工业界建立新的推荐系统基准标准。平台地址：https://rboard.org，演示视频：https://bit.ly/rboard-demo。
原文摘要 · Abstract (English)
Recommender systems research lacks standardized benchmarks for reproducibility and algorithm comparisons. We introduce RBoard, a novel framework addressing these challenges by providing a comprehensive platform for benchmarking diverse recommendation tasks, including CTR prediction, Top-N recommendation, and others. RBoard's primary objective is to enable fully reproducible and reusable experiments across these scenarios. The framework evaluates algorithms across multiple datasets within each task, aggregating results for a holistic performance assessment. It implements standardized evaluation protocols, ensuring consistency and comparability. To facilitate reproducibility, all user-provided code can be easily downloaded and executed, allowing researchers to reliably replicate studies and build upon previous work. By offering a unified platform for rigorous, reproducible evaluation across various recommendation scenarios, RBoard aims to accelerate progress in the field and establish a new standard for recommender systems benchmarking in both academia and industry. The platform is available at https://rboard.org and the demo video can be found at https://bit.ly/rboard-demo.
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