  A key distinguishing feature of conversational recommender systems over
traditional recommender systems is their ability to elicit user preferences
using natural language. Currently, the predominant approach to preference
elicitation is to ask questions directly about items or item attributes. These
strategies do not perform well in cases where the user does not have sufficient
knowledge of the target domain to answer such questions. Conversely, in a
shopping setting, talking about the planned use of items does not present any
difficulties, even for those that are new to a domain. In this paper, we
propose a novel approach to preference elicitation by asking implicit questions
based on item usage. Our approach consists of two main steps. First, we
identify the sentences from a large review corpus that contain information
about item usage. Then, we generate implicit preference elicitation questions
from those sentences using a neural text-to-text model. The main contributions
of this work also include a multi-stage data annotation protocol using
crowdsourcing for collecting high-quality labeled training data for the neural
model. We show that our approach is effective in selecting review sentences and
transforming them to elicitation questions, even with limited training data.
Additionally, we provide an analysis of patterns where the model does not
perform optimally.
