As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.

翻译：作为信息过滤服务，推荐系统通过提供个性化建议并辅助决策，极大地丰富了人们的日常生活，使其在信息时代对人类社会中至关重要且不可或缺。然而，随着人们对推荐系统的依赖日益加深，近期研究表明，由于其存在不公平性（例如职位推荐中的性别歧视），推荐系统可能对社会和个人产生无意的负面影响。为构建可信服务，设计能够缓解这些偏差问题的公平性感知推荐系统至关重要。在本综述中，我们总结了推荐系统中公平性的现有方法与实践。首先，我们阐述了不同推荐场景下公平性的概念，全面分类了当前进展，并介绍了在推荐系统各阶段促进公平性的典型方法。其次，在介绍用于评估推荐系统公平性的数据集与评价指标后，我们将深入探讨公平性感知推荐系统对实际工业应用产生的显著影响。随后，我们强调公平性与可信推荐系统其他原则之间的联系，旨在倡导公平性的同时整体考量可信原则。最后，我们总结本综述，重点指出在理解概念、框架、准确性与公平性权衡以及与可信度的关联方面的潜在机遇，最终目标是推动公平性感知推荐系统的发展。