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A Belief Function Reasoning Approach to Web User Profiling

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Intelligent Data Engineering and Automated Learning – IDEAL 2015 (IDEAL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9375))

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Abstract

This paper presents a novel approach to web user profiling. Our proposed approach consists of two main parts. The first part focuses on discovering user interests in a user feedback collection, usually including relevant and irrelevant documents. Frequent pattern mining widely used in data mining community is applied to extract user feedback information. The second part is to represent user profiles. We introduce a novel user profile model based on belief function reasoning. In this model, the user profile is described by a probability distribution over the user feedback information extracted. Experimental results on an information filtering task show that the proposed approach clearly outperforms several baseline methods.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand

    Luepol Pipanmaekaporn & Suwatchai Kamonsantiroj

Authors
  1. Luepol Pipanmaekaporn
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  2. Suwatchai Kamonsantiroj
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Corresponding author

Correspondence to Suwatchai Kamonsantiroj .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

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© 2015 Springer International Publishing Switzerland

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Pipanmaekaporn, L., Kamonsantiroj, S. (2015). A Belief Function Reasoning Approach to Web User Profiling. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_59

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  • DOI: https://doi.org/10.1007/978-3-319-24834-9_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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