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Government-to-Business Personalized e-Services Using Semantic-Enhanced Recommender System

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Electronic Government and the Information Systems Perspective (EGOVIS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6866))

Abstract

The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking ‘one-to-one’ e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semantic-enhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems.

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Authors and Affiliations

  1. Lab of Decision Systems and e-Service Intelligence, Centre for Quantum Computation and Intelligent Systems, School of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia

    Qusai Shambour & Jie Lu

Authors
  1. Qusai Shambour
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  2. Jie Lu
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Editor information

Editors and Affiliations

  1. Center for Applied ICT (CAICT) and Copenhagen Business School (CBS), Howitzvej 60, 2000, Frederiksberg, Denmark

    Kim Normann Andersen

  2. Institute of Legal Information, Theory and Techniques, ITTIG-CNR, Italian National Reserach Council, Via de’ Barucci 20, 50127, Florence, Italy

    Enrico Francesconi

  3. ESI/Informatics, Örebro University, 701 82, Örebro, Sweden

    Åke Grönlund

  4. Faculty of Law, Leibniz Center for Law, University of Amsterdam, Kloveniersburgwal 48, 1000BA, Amsterdam, The Netherlands

    Tom M. van Engers

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Shambour, Q., Lu, J. (2011). Government-to-Business Personalized e-Services Using Semantic-Enhanced Recommender System. In: Andersen, K.N., Francesconi, E., Grönlund, Å., van Engers, T.M. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2011. Lecture Notes in Computer Science, vol 6866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22961-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-22961-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22960-2

  • Online ISBN: 978-3-642-22961-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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