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Modeling Relevance Judgement Inspired by Quantum Weak Measurement

机译:量子弱测量启发的相关性判断建模

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Concept in Quantum theory (QT) has been successfully inspired analogous concepts in the field of Information Retrieval (IR). Many IR researchers have employed the QT to investigate cognitive phenomena within user behaviors, and also have verified the existence of quantum-like phenomena in real web search. However, for some complex search task, in which user's information need (IN) is dynamic and hard to be captured, QT currently adopted still can not explain some more complex cognitive phenomena. In this paper, a user experiment is conducted to investigate the variance of relevance judgement, and its results demonstrate that quantum Weak Measurement (WM) is more appropriate than the standard quantum measurement to model relevance judgement. Further, a WM-based session search model (WSM) is presented to model user's dynamic evolving IN. The extensive experiments are tested on the session track of TREC 2013 & 2014 and verify the effectiveness of WSM.
机译:量子理论(QT)中的概念已成功地启发了信息检索(IR)领域中的类似概念。许多IR研究人员已经使用QT来研究用户行为中的认知现象,并且还验证了真实网页搜索中类量子现象的存在。然而,对于某些复杂的搜索任务,其中用户的信息需求(IN)是动态的且难以捕获的,当前采用的QT仍无法解释一些更复杂的认知现象。本文通过用户实验研究了相关性判断的方差,其结果表明,量子弱测量(WM)比标准量子测量更适合用于相关性判断。此外,提出了一种基于WM的会话搜索模型(WSM),以对用户的动态演进IN进行建模。广泛的实验在TREC 2013和2014的会议轨道上进行了测试,并验证了WSM的有效性。

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