A quasi-current representation for information needs inspired by Two-State Vector Formalism

Panpan Wang, Yuexian Hou, Jingfei Li, Yazhou Zhang, Dawei Song and Wenjie Li

Physica A: Statistical Mechanics and its Applications, 2017, vol. 482, issue C, 627-637

Abstract: Recently, a number of quantum theory (QT)-based information retrieval (IR) models have been proposed for modeling session search task that users issue queries continuously in order to describe their evolving information needs (IN). However, the standard formalism of QT cannot provide a complete description for users’ current IN in a sense that it does not take the ‘future’ information into consideration. Therefore, to seek a more proper and complete representation for users’ IN, we construct a representation of quasi-current IN inspired by an emerging Two-State Vector Formalism (TSVF). With the enlightenment of the completeness of TSVF, a “two-state vector” derived from the ‘future’ (the current query) and the ‘history’ (the previous query) is employed to describe users’ quasi-current IN in a more complete way. Extensive experiments are conducted on the session tracks of TREC 2013 & 2014, and show that our model outperforms a series of compared IR models.

Keywords: Information Retrieval; Two-State Vector Formalism; Quantum theory; Session search (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:482:y:2017:i:c:p:627-637

DOI: 10.1016/j.physa.2017.04.145

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