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Conversational recommendation system (CRS) is emerging as a user-friendly wayto capture users' dynamic preferences over candidate items and attributes.Multi-shot CRS is designed to make recommendations multiple times until theuser either accepts the recommendation or leaves at the end of their patience.Existing works are trained with reinforcement learning (RL), which may sufferfrom unstable learning and prohibitively high demands for computing. In thiswork, we propose a simple and efficient CRS, MInimalist Non-reinforcedInteractive COnversational Recommender Network (MINICORN). MINICORN models theepistemic uncertainty of the estimated user preference and queries the user forthe attribute with the highest uncertainty. The system employs a simple networkarchitecture and makes the query-vs-recommendation decision using a singlerule. Somewhat surprisingly, this minimalist approach outperformsstate-of-the-art RL methods on three real-world datasets by large margins. Wehope that MINICORN will serve as a valuable baseline for future research.

Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference