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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2017: Intelligent Information and Database Systems pp 269–277Cite as

A Method for User Profile Learning in Document Retrieval System Using Bayesian Network

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

Abstract

User modeling methods are developed by many researches in area of document retrieval systems. The main reason is that the system can not present the same results for every user. Each user can have different information needs even if he uses the same terms to formulate his query. In this paper we present the solution for the problem. We propose a method for user profile building and updating using Bayesian network approaches which allows to discover dependencies between terms. Additionally, we use domain ontology of terms to simplify the calculations. Performed experiments have shown that the quality of presented methods is promising.

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Acknowledgments

This research was partially supported by Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wrocław, Poland

    Bernadetta Maleszka

Authors
  1. Bernadetta Maleszka
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Correspondence to Bernadetta Maleszka .

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Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Maleszka, B. (2017). A Method for User Profile Learning in Document Retrieval System Using Bayesian Network. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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