  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.
