Plausible Repairs for Inconsistent Requirements
Proceedings of the 21st International Joint Conference on Artificial Intelligence, page 791-796. Pasadena, California, USA, (2009)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.
