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

TL;DR

RBoard is a comprehensive platform that standardizes benchmarking for recommender systems, promoting reproducibility and comparability across diverse recommendation tasks and datasets.

Contribution

It introduces a unified framework for reproducible and reusable benchmarking of recommender algorithms across multiple tasks and datasets.

Findings

Provides standardized evaluation protocols for recommendation tasks.

Enables easy reproduction of experiments with downloadable code.

Facilitates holistic performance assessment across datasets.

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.