  With the recent advances of conversational recommendations, the recommender
system is able to actively and dynamically elicit user preference via
conversational interactions. To achieve this, the system periodically queries
users' preference on attributes and collects their feedback. However, most
existing conversational recommender systems only enable the user to provide
absolute feedback to the attributes. In practice, the absolute feedback is
usually limited, as the users tend to provide biased feedback when expressing
the preference. Instead, the user is often more inclined to express comparative
preferences, since user preferences are inherently relative. To enable users to
provide comparative preferences during conversational interactions, we propose
a novel comparison-based conversational recommender system. The relative
feedback, though more practical, is not easy to be incorporated since its
feedback scale is always mismatched with users' absolute preferences. With
effectively collecting and understanding the relative feedback from an
interactive manner, we further propose a new bandit algorithm, which we call
RelativeConUCB. The experiments on both synthetic and real-world datasets
validate the advantage of our proposed method, compared to the existing bandit
algorithms in the conversational recommender systems.
