Constraint sets can become inconsistent in different contexts. We are interested in identifying minimal sets of constraints that have to be adapted or deleted in order to restore consistency. In this paper we sketch a highly efficient divide-and-conquer based diagnosis approach which identifies minimal sets of faulty constraints in a given over-constrained problem. This approach is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial.

Configuration technologies are well established as a foundation of mass customization which is a production paradigm that supports the manufacturing of highly-variant products under pricing conditions similar to mass production. A side-effect of the high diversity of products offered by a configurator is that the complexity of the alternatives may outstrip a user's capability to explore them and make a buying decision. In order to improve the quality of configuration processes, we combine knowledge-based configuration with collaborative and content-based recommendation algorithms. In this paper we present configuration techniques that recommend personalized default values to users. Results of an empirical study show improvements in terms of, for example, user satisfaction or the quality of the configuration process.

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.