A theory of diagnosis from first principles
Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Communications of the ACM
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A comparison of complete global optimization solvers
Mathematical Programming: Series A and B
Debugging user interface descriptions of knowledge-based recommender applications
Proceedings of the 11th international conference on Intelligent user interfaces
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Proceedings of the 13th international conference on Intelligent user interfaces
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
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Generating and evaluating evaluative arguments
Artificial Intelligence
Multivariate preference models and decision making with the MAUT machine
UM'03 Proceedings of the 9th international conference on User modeling
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Hybrid web recommender systems
The adaptive web
Persuasive recommendation: serial position effects in knowledge-based recommender systems
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
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Constraint-based recommender systems support customers in preference construction processes related to complex products and services. In this context, utility constraints scoring rules play an important role. They determine the order in which items products and services are presented to customers. In many cases utility constraints are faulty, i.e., calculate rankings which are not expected and accepted by marketing and sales experts. The adaptation of these constraints is extremely time-consuming and often an error-prone process. We present an approach to the automated adaptation of utility constraint sets which is based on solutions for nonlinear optimization problems. This approach increases the applicability of constraint-based recommendation technologies by allowing the automated reproduction of example item rankings specified by marketing and sales experts.