为推荐系统研究打造可解释的社区化数据集搜索引擎。
DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research
- 支持多属性语义搜索，涵盖名称、描述和推荐领域。
- 提供搜索相关性解释，提升结果透明度。
- 鼓励社区贡献标准化元数据，适合科研复现者使用。
推荐系统的研究与开发离不开合适的数据集，但因数据源分散、元数据不一致，精准查找特定任务或领域的数据集仍具挑战。为此，我们提出一个面向推荐系统研究的社区驱动且可解释的数据集搜索引擎。该系统支持跨多个数据集属性（如名称、描述、推荐领域）的语义搜索，并提供搜索结果的相关性解释以增强透明性。通过允许用户在公共仓库中贡献标准化的元数据，系统鼓励社区参与。该平台显著提升了数据集的可发现性与搜索可解释性，有助于更高效的科研复现。平台已公开：https://ds4rs.com。
原文摘要 · Abstract (English)
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.
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