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User Preference Through Learning User Profile for Ubiquitous Recommendation Systems

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4251))

Abstract

As ubiquitous commerce is coming, the ubiquitous recommendation systems utilize collaborative filtering to help users with fast searches for the best suitable items by analyzing the similar preference. However, collaborative filtering may not provide high quality recommendation because it does not consider user’s preference on the attribute, the first rater problem, and the sparsity problem. This paper proposes the user preference through learning user profile for ubiquitous recommendation systems to solve the current problems. In addition, to determine the similarity between the users belonging to particular categories and new users, we assign different statistical values to the preference through learning user profile. We evaluated the proposed method on the EachMovie dataset and it was found to significantly outperform the previously proposed method.

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Author information

Authors and Affiliations

  1. School of Computer Information Engineering, Sangji University, Korea

    Kyung-Yong Jung

Authors
  1. Kyung-Yong Jung
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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Jung, KY. (2006). User Preference Through Learning User Profile for Ubiquitous Recommendation Systems. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_20

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  • DOI: https://doi.org/10.1007/11892960_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46535-5

  • Online ISBN: 978-3-540-46536-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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