intelligent debugging and repair of utility constraint sets in knowledge-based recommender applications
Recommenders support effective product retrieval processes for online users. These systems propose repair actions in situations where no solution can be found and derive recommendations including a set of explanations as to why a certain product has been selected. In this context utility constraints (scoring rules) have to be defined which specify the way utilities of products, explanations, and repair alternatives are determined. Such constraints can be faulty which means that they calculate rankings in a way not expected by marketing and sales experts. The maintenance and repair of such constraints is an extremely error-prone task. In this paper we present an intelligent environment which supports the automated adaptation of faulty utility constraints taking into account existing marketing and sales requirements. In this context we discuss experiences from commercial projects.
knowledge-based recommenders knowledge acquisition multi-attribute utility theory (maut)