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, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, 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, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.

翻译：咨询系统可以有力地影响我们在网上看到的信息,例如社交媒体信息,从而影响我们的信仰、决定和行动。同时,这些系统可以为不同的利益攸关方创造巨大的商业价值。鉴于这种基于AI的系统对个人、组织和社会的潜在影响越来越大,近年来公平问题日益受到重视。然而,关于建议系统公平性的研究仍然是一个发展中的领域。在本次调查中,我们首先审查最近在该领域提出的基本概念和公平概念。随后,我们调查了这一领域研究目前是如何运作的,例如一般研究方法、公平度量度和算法方法。总体而言,我们对近期工作的分析表明某些研究差距。特别是,我们发现,在计算机科学的许多研究工作中,普遍存在非常抽象的操作问题,这回避了在特定应用中什么是公平建议的基本和重要问题。