Recommender systems are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to increasing attention to fairness-aware and diversity-aware recommender systems. While most existing studies explore fairness and diversity independently, we identify strong connections between these two domains. In this survey, we first discuss each of them individually and then dive into their connections. Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level. With this expanded perspective on user and item-level diversity, we re-interpret fairness studies from the viewpoint of diversity. This fresh perspective enhances our understanding of fairness-related work and paves the way for potential future research directions. Papers discussed in this survey along with public code links are available at https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems .

翻译：推荐系统是缓解信息过载的有效工具，已在多个领域得到广泛应用。然而，仅专注于效用目标已不足以应对现实世界中的问题，因此公平性感知和多样性感知的推荐系统日益受到关注。尽管现有研究大多独立探讨公平性与多样性，但我们发现这两个领域之间存在紧密联系。本综述首先分别讨论每个概念，随后深入分析其关联性。此外，受用户级与物品级公平性概念的启发，我们将多样性的理解从物品层面扩展至用户层面。基于这一对用户级与物品级多样性的拓展视角，我们从多样性的角度重新解读公平性研究。这一新视角增进了对公平性相关工作的理解，并为未来潜在研究方向奠定了基础。本综述中讨论的论文及公开代码链接见 https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems。