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：作为机器学习最广泛的应用之一，推荐系统在辅助人类决策中扮演着重要角色。用户满意度和平台利益与生成的推荐结果质量密切相关。然而，作为高度数据驱动的系统，推荐系统可能受到数据或算法偏差的影响，从而产生不公平的结果，这可能削弱系统的可信度。因此，解决推荐场景中的潜在不公平问题至关重要。近年来，越来越多关于推荐系统公平性研究的工作涌现，相关方法不断增多，但这些研究较为分散且缺乏系统性组织，使得该领域的新研究者难以深入理解。这促使我们对推荐系统中的公平性现有工作进行了系统综述。本综述聚焦于推荐系统公平性文献中的基础性问题。首先简要介绍机器学习基本任务（如分类和排序）中的公平性概念，以提供公平性研究的总体概述，同时引入在推荐系统中研究公平性时需考虑的复杂情况与挑战。随后，综述将重点介绍推荐系统中的公平性，涵盖当前公平性定义的分类体系、改进公平性的典型技术，以及用于公平性研究的数据集。最后，本综述探讨了公平性研究中的挑战与机遇，期望推动公平推荐研究领域乃至更广泛领域的发展。