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Analysis of Profile Convergence in Personalized Document Retrieval Systems

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Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • user profile
  • user preference
  • profile convergence
  • evaluating retrieval systems

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  • DOI: 10.1007/978-3-319-11289-3_7
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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)