In information retrieval, relevance is perceived as a multidimensional and dynamic concept influenced by user, task, and domain factors. Relying on this perspective, researchers have introduced multidimensional relevance models addressing diverse search tasks across numerous knowledge domains. Through our systematic review of 72 studies, we categorize research based on domain specificity and the distinct relevance aspects employed for estimating multidimensional relevance. Moreover, we highlight the approaches used to aggregate scores related to these factors and rank information items. Our insights underline the importance of concise definitions and unified methods for estimating relevance factors within and across domains. Finally, we identify benchmark collections for evaluations based on multiple relevance aspects while underscoring the necessity for new ones. Our findings suggest that large language models hold considerable promise for shaping future research in this field, mainly due to their relevance labeling abilities.This article is categorized under: Application Areas > Science and Technology Technologies > Computational Intelligence

中文翻译：

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信息检索中多维相关性估计的系统综述

在信息检索中，相关性被视为一个受用户、任务和领域因素影响的多维动态概念。依靠这个观点，研究人员引入了多维相关性模型来解决跨多个知识领域的各种搜索任务。通过对 72 项研究的系统回顾，我们根据领域特异性和用于估计多维相关性的不同相关性方面对研究进行分类。此外，我们重点介绍了用于汇总与这些因素相关的分数并对信息项进行排名的方法。我们的见解强调了简洁的定义和统一的方法对于估计领域内和跨领域的相关因素的重要性。最后，我们根据多个相关方面确定了评估基准集合，同时强调了新基准集合的必要性。我们的研究结果表明，大型语言模型对于塑造该领域的未来研究具有相当大的前景，这主要是由于它们的相关性标记能力。本文分类如下： 应用领域 > 科学技术 技术 > 计算智能