  Conversational recommendation systems elicit user preferences by interacting
with users to obtain their feedback on recommended commodities. Such systems
utilize a multi-armed bandit framework to learn user preferences in an online
manner and have received great success in recent years. However, existing
conversational bandit methods have several limitations. First, they only enable
users to provide explicit binary feedback on the recommended items or
categories, leading to ambiguity in interpretation. In practice, users are
usually faced with more than one choice. Relative feedback, known for its
informativeness, has gained increasing popularity in recommendation system
design. Moreover, current contextual bandit methods mainly work under linear
reward assumptions, ignoring practical non-linear reward structures in
generalized linear models. Therefore, in this paper, we introduce relative
feedback-based conversations into conversational recommendation systems through
the integration of dueling bandits in generalized linear models (GLM) and
propose a novel conversational dueling bandit algorithm called ConDuel.
Theoretical analyses of regret upper bounds and empirical validations on
synthetic and real-world data underscore ConDuel's efficacy. We also
demonstrate the potential to extend our algorithm to multinomial logit bandits
with theoretical and experimental guarantees, which further proves the
applicability of the proposed framework.
