A theory of diagnosis from first principles
Artificial Intelligence
Model-based diagnosis of hardware designs
Artificial Intelligence
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
Koba4MS: Selling Complex Products and Services Using Knowledge-Based Recommender Technologies
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
A survey of intelligent debugging
AI Communications
A prototype for model-based on board diagnosis of automotive systems
AI Communications
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Complex assortments of products and services offered by online selling platforms require the provision of sales support systems assisting customers in the product selection process. Knowledge-based recommenders are intelligent sales assistance systems which guide online customers through personalized sales dialogs and automatically determine products which conform to their needs and wishes. Such systems have been successfully applied in a number of application domains such as financial services or digital cameras. In this context, the construction of recommender user interfaces is still a challenging task. In many cases faulty models of recommender user interfaces are defined by knowledge engineers and no automated support for debugging such models is available. In this paper we discuss a formal model for defining the intended behaviour of recommender user interfaces and show the application of model-based diagnosis concepts which allow the automated debugging of those definitions. Experiences show that this approach significantly increases the productivity of recommender user interface development and maintenance.