Towards a theoretical formalization of conversational recommendation

Tools that interact vocally with users are becoming increasingly popular in the market, boosting industry and academia interest in them. In such environments, conversational recommender systems succeed in guiding users in situations of information overload. Through multiple interactions with users, such systems ask questions, filter the catalog in a personalized manner, and suggest items that are of potential interest to the consumer. In this context, conversational efficiency in terms of the number of required interactions often plays a fundamental role. This work introduces a theoretical and domain independent approach to support the efficiency analysis of a conversational recommendation engine. Observations from an empirical analysis align with our theoretical findings.