Generating Recommendation Dialogues from Product Models

Contents

For complex and frequently changing product domains, the maintenance of an electronic recommender system is a time- and money-consuming task, as the man-machine interface has to be adapted to the product model any time the latter is changed. Ideally, changes in the model would lead to an automatic adaptation of the recommendation dialogue without much overhead. In this paper we present an approach to generate an elaborate dialogue from a given product model, ensuring efficient reaction to changes of the model. Using statecharts to structure the dialogue, an intuitive, easily visualizable internal representation can be inferred that is able to handle all principal functions required of a recommender system, such as dynamic dialogue management, belief revision and the generation of recommendations.