RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks

• URL: http://arxiv.org/abs/2409.05526v2
• Date: Tue, 10 Sep 2024 16:46:10 GMT
• Title: RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks
• Authors: Xinyang Shao, Edoardo D'Amico, Gabor Fodor, Tri Kurniawan Wijaya,
• Abstract summary: RBoard is a novel framework for benchmarking recommender systems. It provides a comprehensive platform for benchmarking diverse recommendation tasks, including CTR prediction, Top-N recommendation, and others. The framework evaluates algorithms across multiple datasets within each task, aggregating results for a holistic performance assessment.
• Score: 0.4312340306206883
• License:
• Abstract: 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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