A systematic review of multidimensional relevance estimation in information retrieval

Georgios Peikos, Gabriella Pasi
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Abstract

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
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信息检索中的多维相关性估计系统回顾
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。基于这一观点,研究人员引入了多维相关性模型,以应对众多知识领域的不同搜索任务。通过对 72 项研究的系统回顾,我们根据领域的特殊性和估算多维相关性所采用的不同相关性方面对研究进行了分类。此外,我们还强调了用于汇总与这些因素相关的分数并对信息项目进行排序的方法。我们的见解强调了简明定义和统一方法对于估算领域内和跨领域相关性因素的重要性。最后,我们确定了基于多个相关性方面的评估基准集合,同时强调了新基准集合的必要性。我们的研究结果表明,大型语言模型在这一领域的未来研究中大有可为,这主要归功于它们的相关性标注能力。本文分类:应用领域 > 科学与技术技术 > 计算智能
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