FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients

arXiv:2405.02881v1 Announce Type: new
Abstract: Conversational recommender systems have emerged as a potent solution for efficiently eliciting user preferences. These systems interactively present queries associated with "key terms" to users and leverage user feedback to estimate user preferences more efficiently. Nonetheless, most existing algorithms adopt a centralized approach. In this paper, we introduce FedConPE, a phase elimination-based federated conversational bandit algorithm, where $M$ agents collaboratively solve a global contextual linear bandit problem with the help of a central server while …

abstract algorithms arxiv conversational cs.ai cs.lg feedback key paper queries recommender systems solution stat.ml systems terms type user feedback