An adaptive contextual quantum language model

Li, Jingfei; Zhang, Peng; Song, Dawei and Hou, Yuexian (2016). An adaptive contextual quantum language model. Physica A: Statistical Mechanics and its Applications, 456 pp. 51–67.



User interactions in search system represent a rich source of implicit knowledge about the user’s cognitive state and information need that continuously evolves over time. Despite of massive efforts that have been made to exploiting and incorporating this implicit knowledge in information retrieval, it is still a challenge to effectively capture the term dependencies and the user’s dynamic information need (reflected by query modifications) in the context of user interaction. To tackle these issues, motivated by the recent Quantum Language Model (QLM), we develop a QLM based retrieval model for session search, which naturally incorporates the complex term dependencies occurring in user’s historical queries and clicked documents with density matrices. In order to capture the dynamic information within users’ search session, we propose a density matrix transformation framework and further develop an adaptive QLM ranking model. Extensive comparative experiments show the effectiveness of our session quantum language models.

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  • Item ORO ID
  • 45876
  • Item Type
  • Journal Item
  • ISSN
  • 0378-4371
  • Project Funding Details
  • Funded Project NameProject IDFunding Body
    Key Basic Research Project, 973 Program2013CB329304Chinese National Program
    Key Basic Research Project, 973 Program2014CB744604Chinese National Program
    Chinese 863 Program2015AA015403Not Set
    Not Set61272265Natural Science Foundation of China
    Not Set61402324Natural Science Foundation of China
    Tianjin Research Program of Application Foundation and Advanced Technology15JCQNJC41700Not Set
  • Keywords
  • Quantum language model; Density matrix transformation; Session search; Query change information
  • Academic Unit or School
  • Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
    Faculty of Science, Technology, Engineering and Mathematics (STEM)
  • Copyright Holders
  • © 2016 Elsevier
  • Depositing User
  • Dawei Song