Adaptive Utility-Based Recommendation

  • Alexander Felfernig
  • Monika Mandl
  • Stefan Schippel
  • Monika Schubert
  • Erich Teppan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6096)


Knowledge-based recommenders support customers in preference construction processes related to complex products and services. In this context, utility constraints (scoring rules) play an important role. They determine the order in which items (products and services) are presented to customers. In many cases utility constraints are faulty, i.e., calculate rankings which are not expected and accepted by marketing and sales experts. The adaptation of these constraints is extremely time-consuming and often an error-prone process. In this paper we present an approach which effectively supports the automated adaptation of utility constraint sets based on solutions for corresponding nonlinear optimization problems. This approach significantly increases the applicability of knowledge-based recommendation by allowing the automated reproduction of item rankings specified by marketing and sales experts.


Utility-based recommendation non-linear optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Monika Mandl
    • 1
  • Stefan Schippel
    • 2
  • Monika Schubert
    • 1
  • Erich Teppan
    • 2
  1. 1.Applied Software EngineeringGraz University of TechnologyGrazAustria
  2. 2.Intelligent Systems and Business InformaticsUniversity KlagenfurtKlagenfurtAustria

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