Plausible repairs for inconsistent requirements

  • Authors:
  • Alexander Felfernig;Gerhard Friedrich;Monika Schubert;Monika Mandl;Markus Mairitsch;Erich Teppan

  • Affiliations:
  • Applied Software Engineering, Graz University of Technology;Intelligent Systems and Business Informatics, University of Klagenfurt;Applied Software Engineering, Graz University of Technology;Applied Software Engineering, Graz University of Technology;Intelligent Systems and Business Informatics, University of Klagenfurt;Intelligent Systems and Business Informatics, University of Klagenfurt

  • Venue:
  • IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
  • Year:
  • 2009

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Abstract

Knowledge-based recommenders support users in the identification of interesting items from large and potentially complex assortments. In cases where no recommendation could be found for a given set of requirements, such systems propose explanations that indicate minimal sets of faulty requirements. Unfortunately, such explanations are not personalized and do not include repair proposals which triggers a low degree of satisfaction and frequent cancellations of recommendation sessions. In this paper we present a personalized repair approach that integrates the calculation of explanations with collaborative problem solving techniques. In order to demonstrate the applicability of our approach, we present the results of an empirical study that show significant improvements in the accuracy of predictions for interesting repairs.