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International Conference on Computational Collective Intelligence

ICCCI 2014: Computational Collective Intelligence. Technologies and Applications pp 62–71Cite as

Analysis of Profile Convergence in Personalized Document Retrieval Systems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8733))

Abstract

Modeling user interests in personalized document retrieval system is currently a very important task. The system should gather information about the user to recommend him better results. In this paper a mathematical model of user preference and profile is considered. The main assumption is that the system does not know the preference. The main aim of the system is to build a profile close to user preference based on observations of user activities. The method for building and updating user profile is presented and a model of simulation user behaviour in such system is proposed. The analytical properties of this method are considered and two theorems are presented and proved.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Bernadetta Maleszka

Authors
  1. Bernadetta Maleszka
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Korea

    Dosam Hwang  & Jason J. Jung  & 

  2. Institute of Informatics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc-Thanh Nguyen

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© 2014 Springer International Publishing Switzerland

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Maleszka, B. (2014). Analysis of Profile Convergence in Personalized Document Retrieval Systems. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-11289-3_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11288-6

  • Online ISBN: 978-3-319-11289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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