Relevance estimation is one of the core concerns of information retrieval (IR) studies. Although existing retrieval models gained much success in both deepening our understanding of information seeking behavior and building effective retrieval systems, we have to admit that the models work in a rather different manner from how humans make relevance judgments. Users’ information seeking behaviors involve complex cognitive processes, however, the majority of these behavior patterns are not considered in existing retrieval models. To bridge the gap between practical user behavior and retrieval model, it is essential to systematically investigate user cognitive behavior during relevance judgement and incorporate these heuristics into retrieval models. In this paper, we aim to formally define a set of basic user reading heuristics during relevance judgement and investigate their corresponding modeling strategies in retrieval models. Further experiments are conducted to evaluate the effectiveness of different reading heuristics for improving ranking performance. Based on a large-scale Web search dataset, we find that most reading heuristics can improve the performance of retrieval model and establish guidelines for improving the design of retrieval models with human-inspired heuristics. Our study sheds light on building retrieval model from the perspective of cognitive behavior.

相关性估计是信息检索 (IR) 研究的核心问题之一。尽管现有的检索模型在加深我们对信息搜索行为的理解和构建有效的检索系统方面都取得了很大的成功，但我们不得不承认，这些模型的工作方式与人类进行相关性判断的方式截然不同。用户的信息搜索行为涉及复杂的认知过程，但现有的检索模型并未考虑这些行为模式中的大部分。为了弥合实际用户行为与检索模型之间的差距，必须在相关性判断过程中系统地调查用户认知行为，并将这些启发式方法纳入检索模型。在本文中，我们的目标是在相关性判断过程中正式定义一组基本的用户阅读启发式方法，并研究它们在检索模型中的相应建模策略。进行了进一步的实验以评估不同阅读启发式方法对提高排名性能的有效性。基于大规模的网络搜索数据集，我们发现大多数阅读启发式可以提高检索模型的性能，并为改进具有启发式启发式的检索模型的设计建立指导方针。我们的研究揭示了从认知行为的角度构建检索模型。基于大规模的网络搜索数据集，我们发现大多数阅读启发式可以提高检索模型的性能，并为改进具有启发式启发式的检索模型的设计建立指导方针。我们的研究揭示了从认知行为的角度构建检索模型。基于大规模的网络搜索数据集，我们发现大多数阅读启发式可以提高检索模型的性能，并为改进具有启发式启发式的检索模型的设计建立指导方针。我们的研究揭示了从认知行为的角度构建检索模型。