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.

翻译：推荐系统能够强烈影响我们在线上（如社交媒体）看到的信息，从而影响我们的信念、决策和行动。同时，这些系统能为不同利益相关者创造显著的商业价值。鉴于这种基于人工智能的系统对个人、组织和社会日益增长的影响力，近年来公平性问题获得了更多关注。然而，推荐系统中的公平性研究仍是一个发展中的领域。在本综述中，我们首先回顾了该领域近期提出的公平性基本概念与定义。随后，通过审视160余篇学术文献，我们从通用研究方法、公平性度量及算法方法等方面，概述了该领域当前的研究实践。整体而言，我们的分析指出了若干研究空白。特别地，我们发现许多计算机科学研究中存在高度抽象的问题操作化方式，且对于支撑性的规范主张以及在特定应用背景下何为公平推荐的深层问题，往往缺乏深入探讨。这些观察表明，需要更多跨学科研究，以更全面且更具影响力的方式解决推荐中的公平性问题。