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Multivariate Preference Models and Decision Making with the MAUT Machine

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User Modeling 2003 (UM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2702))

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Abstract

With the advent of e-commerce, systems supporting the user in finding just the right product in an electronic catalog have gained increasing attention. While collaborative recommender systems (RS) derive their suggestions from other users’ opinions, structure-based systems assess a product according to how well its properties satisfy a user’s preferences. This paper presents the MAUT Machine, a system implementing the basic machinery to be used by a structure-based RS to elicit and maintain complex user preference models and evaluate the entries of an electronic catalog according to their appropriateness for a given user or group of users.

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Author information

Authors and Affiliations

  1. DFKI, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Christian Schmitt, Dietmar Dengler & Mathias Bauer

Authors
  1. Christian Schmitt
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  2. Dietmar Dengler
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  3. Mathias Bauer
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Editor information

Editors and Affiliations

  1. School of Information Sciences, Department of Information Science and Telecommunications, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, 15260, USA

    Peter Brusilovsky

  2. Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Albert Corbett

  3. Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabona 4, 70126, Bari, Italy

    Fiorella de Rosis

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© 2003 Springer-Verlag Berlin Heidelberg

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Schmitt, C., Dengler, D., Bauer, M. (2003). Multivariate Preference Models and Decision Making with the MAUT Machine. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds) User Modeling 2003. UM 2003. Lecture Notes in Computer Science(), vol 2702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44963-9_40

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  • DOI: https://doi.org/10.1007/3-540-44963-9_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40381-4

  • Online ISBN: 978-3-540-44963-8

  • eBook Packages: Springer Book Archive

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