Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications. Recent trends in RSs have revealed a major paradigm shift, moving the spotlight from model-centric innovations to data-centric efforts (e.g., improving data quality and quantity). This evolution has given rise to the concept of data-centric recommender systems (Data-Centric RSs), marking a significant development in the field. This survey provides the first systematic overview of Data-Centric RSs, covering 1) the foundational concepts of recommendation data and Data-Centric RSs; 2) three primary issues of recommendation data; 3) recent research developed to address these issues; and 4) several potential future directions of Data-Centric RSs.

翻译：推荐系统（RSs）已成为缓解各类实际应用中信息过载问题的重要工具。近年来的研究趋势揭示了一场重大范式转变，研究焦点正从以模型为中心的创新转向以数据为中心的努力（例如改进数据质量和数量）。这一演进催生了以数据为中心的推荐系统（Data-Centric RSs）概念，标志着该领域的重大发展。本综述首次系统性地概述了以数据为中心的推荐系统，涵盖以下内容：1）推荐数据及以数据为中心的推荐系统的基础概念；2）推荐数据的三大核心问题；3）为解决这些问题而开展的最新研究进展；以及4）以数据为中心的推荐系统的若干潜在未来发展方向。