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