Publication: Plausible Repairs for Inconsistent Requ...
Master data
| Title: | Plausible Repairs for Inconsistent Requirements |
| Subtitle: | |
| 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. |
| Keywords: |
| Publication type: | Article in compilation (Authorship) |
| Publication date: | 2009 (Print) |
| Published by: |
Proceedings of the 21st International joint Conference on Artificial Intelligence
Proceedings of the 21st International joint Conference on Artificial Intelligence
(
IJCAI Inc.;
)
to publication |
| Title of the series: | - |
| Volume number: | - |
| First publication: | Yes |
| Page: | pp. 791 - 796 |
Versionen
| Keine Version vorhanden |
| Publication date: | 2009 |
| ISBN: | - |
| ISSN: | - |
| Homepage: | - |
Authors
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A. Felfernig
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M. Schubert
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G. Friedrich
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M. Mandl
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M. Mairitsch
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E. Teppan
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| Markus Mairitsch (internal) |
| Monika Mandl (internal) |
| Erich Christian Teppan (internal) |
| Gerhard Friedrich (internal) |
| Alexander Felfernig (internal) |
Assignment
| Organisation | Address | ||||
|---|---|---|---|---|---|
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Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
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AT - A-9020 Klagenfurt |
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