Accessing suitable datasets is critical for research and development in recommender systems. However, finding datasets that match specific recommendation task or domains remains a challenge due to scattered sources and inconsistent metadata. To address this gap, we propose a community-driven and explainable dataset search engine tailored for recommender system research. Our system supports semantic search across multiple dataset attributes, such as dataset names, descriptions, and recommendation domain, and provides explanations of search relevance to enhance transparency. The system encourages community participation by allowing users to contribute standardized dataset metadata in public repository. By improving dataset discoverability and search interpretability, the system facilitates more efficient research reproduction. The platform is publicly available at: https://ds4rs.com.
@article{arxiv.2508.10238,
title = {DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research},
author = {Xinyang Shao and Tri Kurniawan Wijaya},
journal= {arXiv preprint arXiv:2508.10238},
year = {2025}
}