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Using Multi-attribute Structures and Significance Term Evaluation for User Profile Adaptation

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6922))

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Abstract

This contribution presents a new approach to the representation of user’s interests and preferences. The adaptive user profile includes both interests given explicitly by the user, as a query, and also preferences expressed by the valuation of relevance of retrieved documents, so to express field independent translation between terminology used by user and terminology accepted in some field of knowledge. Procedures for building, modifying and using the profile, heuristic-based significant terms selection from relevant documents are presented. Experiments concerning the profile, as a personalization mechanism of Web search system, are presented and discussed.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Poland

    Agnieszka Indyka-Piasecka

Authors
  1. Agnieszka Indyka-Piasecka
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Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Morska 81-87, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

  2. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. University of Information Technology Vietnam, Km 20, Xa lo Ha Noi, Linh Trung, Thu Duc, 848, HCM City, Vietnam

    Kiem Hoang

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Indyka-Piasecka, A. (2011). Using Multi-attribute Structures and Significance Term Evaluation for User Profile Adaptation. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_33

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  • DOI: https://doi.org/10.1007/978-3-642-23935-9_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23934-2

  • Online ISBN: 978-3-642-23935-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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