A theory of diagnosis from first principles
Artificial Intelligence
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Dynamic constraint satisfaction problems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Fast computation of query relaxations for knowledge-based recommenders
AI Communications
Techniques for fast query relaxation in content-based recommender systems
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
An efficient diagnosis algorithm for inconsistent constraint sets
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Generating range fixes for software configuration
Proceedings of the 34th International Conference on Software Engineering
A probabilistic optimization framework for the empty-answer problem
Proceedings of the VLDB Endowment
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Content-based recommenders are systems that exploit detailed knowledge about the items in the catalog for generating adequate product proposals. In that context, query relaxation is one of the basic approaches for dealing with situations, where none of the products in the catalogue exactly matches the customer requirements. The major challenges when applying query relaxation are that the relaxation should be minimal (or optimal for the customer), that there exists a potentially vast search space, and that we have to deal with hard time constraints in interactive recommender applications. In this paper, we show how the task of finding adequate or customer optimal relaxations for a given recommendation problem can be efficiently achieved by applying techniques from the field of model-based diagnosis, i.e., with the help of extended algorithms for computing conflicts and hitting sets. In addition, we propose a best-effort search algorithm based on branch-and-bound for dealing with hard problems and also describe how an optimal relaxation can be immediately obtained when partial queries can be (pre-)evaluated. Finally, we discuss the results of an evaluation of the described techniques, which we made by extending an existing knowledge-based recommender system and which we based on different real-world problem settings.