Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey's main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the open research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.

翻译：使返回结果多样化是检索系统中的重要研究课题，旨在同时满足用户多样化的兴趣和提供方的公平市场曝光。近年来，针对多样性的研究日益受到关注，同时涌现出大量关于在搜索和推荐中促进多样性的方法文献。然而，检索系统中的多样性研究缺乏系统组织且较为碎片化。本综述首次提出统一分类体系，用于对搜索和推荐这两个检索系统中研究最广泛的领域的多样性度量指标与实现方法进行分类。我们首先简要讨论多样性在检索系统中的重要性，继而总结搜索和推荐中不同的多样性关注点，并强调其关联性与差异性。在综述主体部分，我们从搜索和推荐的双重视角出发，提出检索系统中多样性度量指标与方法的统一分类体系。在综述后半部分，我们探讨搜索与推荐中多样性研究的开放性问题，以期启发未来创新，并推动多样性在实际系统中的落地实施。