Conversational Recommendation With Online Learning and Clustering on Misspecified Users
Xiangxiang Dai, Zhiyong Wang, Jize Xie, Xutong Liu, John C. S. Lui
Abstract
In the domain of conversational recommendation systems (CRSs), the development of recommenders capable of eliciting user preferences through conversation has marked a significant advancement. These systems have been enhanced by incorporating conversational key-terms related to items, which streamline the recommendation process by reducing the extensive exploration that traditional interactive recommenders necessitate. Despite these advancements, CRSs still face significant challenges. The vast number of users and the difficulty in accurately capturing preferences lead to persistent inaccuracies, even when direct user interactions are employed to refine the understanding of user preferences. To tackle these challenges, we propose two innovative bandit algorithms: RCLUMB (Robust Clustering of Misspecified Bandits) and RSCLUMB (Robust Set-based Clustering of Misspecified Bandits). These algorithms employ dynamic graphs and evolving cluster sets, respectively, to represent the changing structure of user preferences, thus leveraging collaborative user preferences to accelerate the learning process. Our algorithms are designed to be resilient against errors in preference modeling and the resulting inaccuracies in clustering. We rigorously analyze the performance of our algorithms and establish regret upper bounds of <inline-formula><tex-math notation="LaTeX">$O(\epsilon _*T\sqrt{md\log T} + d\sqrt{mT}\log T)$</tex-math></inline-formula> under milder assumptions than previous works, matching the state-of-the-art results in several degenerate cases. Through extensive experiments on synthetic and real-world datasets, our algorithms demonstrate superior performance over existing algorithms.