As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.

翻译：作为机器学习最普遍的应用之一，推荐系统在辅助人类决策方面扮演着重要角色。用户满意度和平台利益与推荐结果的质量密切相关。然而，作为一种高度数据驱动的系统，推荐系统可能受数据或算法偏差的影响而产生不公平的结果，从而削弱系统的可信度。因此，解决推荐场景中潜在的不公平问题至关重要。近年来，推荐系统中的公平性考量日益受到关注，越来越多文献探索促进推荐公平性的方法。但这些研究较为零散且缺乏系统组织，使得新研究者难以深入理解该领域。这促使我们对现有推荐公平性研究进行系统性综述。本综述聚焦于推荐公平性文献的基础内容。首先简要介绍分类和排序等基础机器学习任务中的公平性，以提供公平性研究的总体概述，并阐述推荐系统中研究公平性时需考虑的复杂情境与挑战。随后，综述将聚焦推荐公平性，介绍当前公平性定义的分类体系、改进公平性的典型技术，以及推荐公平性研究中的数据集。最后，本文探讨公平性研究的挑战与机遇，以期推动公平推荐研究领域及更广泛方向的发展。