Plausible Repairs for Inconsistent Requirements
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. .
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.
@inproceedings{ijcai2009p136,
title = {Plausible Repairs for Inconsistent Requirements},
author = {Felfernig, Alexander and Friedrich, Gerhard and Schubert, Monika and Mandl, Monika and Mairitsch, Markus and Teppan, Erich},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
year = {2009},
url = {https://ijcai.org/proceedings/2009/136},
}
TY - CPAPER TI - Plausible Repairs for Inconsistent Requirements AU - Felfernig, Alexander AU - Friedrich, Gerhard AU - Schubert, Monika AU - Mandl, Monika AU - Mairitsch, Markus AU - Teppan, Erich T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 UR - https://ijcai.org/proceedings/2009/136 ER -Copied
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