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：多样化返回结果是检索系统中满足用户多元兴趣与供给方公平市场曝光的重要研究方向。近年来，多样化感知研究日益受到关注，涌现出大量关于搜索与推荐场景中提升多样性的方法论文献。然而，当前检索系统中的多样化研究缺乏系统化组织，呈现碎片化特征。本综述首次提出统一分类体系，用以归类作为检索系统两大核心研究领域的搜索与推荐中的多样化度量指标及实现方法。我们首先简要探讨多样性在检索系统中的重要性，继而总结搜索与推荐中多样性的不同关注点，重点阐述二者的关联性与差异性。在综述主体部分，我们从搜索与推荐双重视角出发，提出检索系统中多样化度量指标与方法的统一分类框架。综述后半部分讨论了搜索与推荐中多样化感知研究的开放性挑战，以期为未来创新提供启示，并推动多样性在实际系统中的落地应用。