Federated Conversational Recommender Systems

Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee

Abstract: Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users in a conversational manner to learn their current and fine-grained preferences, a CRS can quickly derive recommendations that are relevant and justifiable. However, existing CRSs typically rely on a centralized training and deployment process, which involves collecting and storing explicitly-communicated user preferences in a centralized repository. These fine-grained user preferences are completely human-interpretable and can easily be used to infer sensitive information (e.g., financial status, political stands, and health information) about the user, if leaked or breached. To address the user privacy concerns in CRS, we first define a set of privacy protection guidelines for preserving user privacy then propose a novel federated CRS framework that effectively reduces the risk of exposing user privacy. Through extensive experiments, we show that the proposed framework not only satisfies these user privacy protection guidelines, but also achieves competitive recommendation performance comparing to the state-of-the-art non-private conversational recommendation approach.