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

翻译：推荐系统（RS）已成为缓解现实场景中信息过载的重要工具。近年来，推荐系统领域经历了范式转变，研究重心从以模型为中心的创新转向数据质量与数量的重要性。这一演变催生了以数据中心推荐系统（Data-Centric RS）的概念，标志着该领域的重大发展。本综述首次系统性地概述数据中心推荐系统，涵盖：1）推荐数据与数据中心推荐系统的基础概念；2）推荐数据中的三大主要问题；3）为应对这些问题而发展的最新研究；以及4）数据中心推荐系统的若干潜在未来方向。