A theory of diagnosis from first principles
Artificial Intelligence
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review
Constraint-based recommender systems: technologies and research issues
Proceedings of the 10th international conference on Electronic commerce
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
Relaxations and explanations for quantified constraint satisfaction problems
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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Knowledge-based recommender systems are applications that support users in the process of retrieving items from a complex product assortment (e.g. computers, holiday packages, and financial services). Recommendations are determined on the basis of explicitly defined user requirements which can be interpreted as constraints to be fulfilled by the items stored in a product table. If no solution (item) can be found, existing knowledge-based recommenders propose non-personalized query relaxations and repair actions for the given set of customer requirements that support a recovery from the dead-end. This paper points out how the usability of knowledge-based recommender systems can be improved by introducing the concept of personalized query relaxations and repair actions.