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
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
Originalsprache | englisch |
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Titel | Proceedings of the 21st International Joint Conference on Artificial Intelligence |
Seiten | 791-796 |
Publikationsstatus | Veröffentlicht - 2009 |
Veranstaltung | 21st International Joint Conference on Artificial Intelligence: IJCAI 2009 - Pasadena, USA / Vereinigte Staaten Dauer: 11 Juli 2009 → 17 Juli 2009 |
Konferenz
Konferenz | 21st International Joint Conference on Artificial Intelligence |
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Kurztitel | IJCAI |
Land/Gebiet | USA / Vereinigte Staaten |
Ort | Pasadena |
Zeitraum | 11/07/09 → 17/07/09 |