  Recommender systems can strongly influence which information we see online,
e.g., on social media, and thus impact our beliefs, decisions, and actions. At
the same time, these systems can create substantial business value for
different stakeholders. Given the growing potential impact of such AI-based
systems on individuals, organizations, and society, questions of fairness have
gained increased attention in recent years. However, research on fairness in
recommender systems is still a developing area. In this survey, we first review
the fundamental concepts and notions of fairness that were put forward in the
area in the recent past. Afterward, through a review of more than 160 scholarly
publications, we present an overview of how research in this field is currently
operationalized, e.g., in terms of general research methodology, fairness
measures, and algorithmic approaches. Overall, our analysis of recent works
points to certain research gaps. In particular, we find that in many research
works in computer science, very abstract problem operationalizations are
prevalent and questions of the underlying normative claims and what represents
a fair recommendation in the context of a given application are often not
discussed in depth. These observations call for more interdisciplinary research
to address fairness in recommendation in a more comprehensive and impactful
manner.
