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Modeling multi-query retrieval tasks using density matrix transformation.

Li, Qiuchi; Li, Jingfei; Zhang, Peng and Song, Dawei (2015). Modeling multi-query retrieval tasks using density matrix transformation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 871–874.

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The quantum probabilistic framework has recently been applied to Information Retrieval (IR). A representative is the Quantum Language Model (QLM), which is developed for the ad-hoc retrieval with single queries and has achieved significant improvements over traditional language models. In QLM, a density matrix, defined on the quantum probabilistic space, is estimated as a representation of user's search intention with respect to a specific query. However, QLM is unable to capture the dynamics of user's information need in query history. This limitation restricts its further application on the dynamic search tasks, e.g., session search. In this paper, we propose a Session-based Quantum Language Model (SQLM) that deals with multi-query session search task. In SQLM, a transformation model of density matrices is proposed to model the evolution of user's information need in response to the user's interaction with search engine, by incorporating features extracted from both positive feedback (clicked documents) and negative feedback (skipped documents). Extensive experiments conducted on TREC 2013 and 2014 session track data demonstrate the effectiveness of SQLM in comparison with the classic QLM.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 The Authors
ISBN: 1-4503-3621-3, 978-1-4503-3621-5
Keywords: quantum language model; session search; density matrix transformation
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 44130
Depositing User: Dawei Song
Date Deposited: 24 Aug 2015 08:45
Last Modified: 07 Dec 2018 22:56
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