Conversational recommendation: Theoretical model and complexity analysis

Recommender systems help users find items of interest in situations of information overload in a personalized way, using needs and preferences of individual users. In conver-sational recommendation approaches, the system acquires needs and preferences in an interactive, multi-turn dialog. This is usually driven by incrementally asking users about their preferences about item features or individual items. A central research goal in this context is efficiency, evaluated concerning the number of required interactions until a sat-isfying item is found. Today, research on dialog efficiency is almost entirely empirical, aim-ing to demonstrate, for example, that one strategy for selecting questions to ask the user is better than another one in a given application. This work complements empirical research with a theoretical, domain-independent model of conversational recommendation. This model, designed to cover a range of application scenarios, allows us to investigate the effi-ciency of conversational approaches in a formal way, particularly concerning the computa-tional complexity of devising optimal interaction strategies. An experimental evaluation empirically confirms our findings.(c) 2022 Elsevier Inc. All rights reserved.