Improving The User Experience With A Conversational Recommender System

Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. Generally, they have an interaction with users based on natural language, buttons, or both. In this paper we study the user interaction with a content-based recommender system implemented as a Telegram chatbot. More specifically, we investigate on one hand what are the best strategies for reducing the cost of interaction for the users and, on the other hand how to improve their experience. Our chatbot is able to provide personalized recommendations in the movie domain and implements critiquing strategies for improving the recommendation accuracy as well. In a preliminary experimental evaluation, carried out through a user study, interesting results emerged.