A theory of diagnosis from first principles
Artificial Intelligence
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
Debugging Incoherent Terminologies
Journal of Automated Reasoning
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Representative explanations for over-constrained problems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Plausible repairs for inconsistent requirements
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Solving Over-constrained Problems Using Network Analysis
ICAIS '09 Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems
ReAction: personalized minimal repair adaptations for customer requests
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Personalized diagnoses for inconsistent user requirements
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
SmartFixer: fixing software configurations based on dynamic priorities
Proceedings of the 17th International Software Product Line Conference
The route to success: a performance comparison of diagnosis algorithms
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
Constraint-based recommender systems support users in the identification of interesting items from large and potentially complex assortments. Within the scope of such a preference construction process, users are repeatedly defining and revising their requirements. As a consequence situations occur where none of the items completely fulfills the set of requirements and the question has to be answered which is the minimal set of requirements that has to be changed in order to be able to find a recommendation. The identification of such minimal sets relies heavily on the identification of minimal conflict sets. Existing conflict detection algorithms are not exploiting the basic structural properties of constraint-based recommendation problems. In this paper we introduce the FastXplain conflict detection algorithm which shows a significantly better performance compared to existing conflict detection algorithms. In order to demonstrate the applicability of our algorithm we report the results of a corresponding performance evaluation.