Conversational recommendations using model-based reasoning

Chatbots as conversational recommender have gained increasing importance over the years. The chatbot market offers a variety of applications for research and industry alike. In this paper, we discuss an implementation that supports the use of our recommendation algorithm during chatbot communication. The program eases communication and improves the underlying recommendation flow. In particular, the implementation makes use of our model-based reasoning approach for improving user experience during a chat, i.e., in cases where user configurations cause inconsistencies. The approach deals with such issues by removing inconsistencies in order to generate a valid recommendation. In addition to the underlying definitions, we demonstrate our implementation along use cases from the tourism domain.