IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History

arXiv:2403.16075v1 Announce Type: new
Abstract: Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such as streaming recommender systems, online decision-makers typically engage in online learning during the decision-making process, meaning that the interaction history generated by online decision-makers includes their behavioral evolution from novice expert to experienced expert. This poses a new challenge for existing imitation learning approaches that …

abstract applications arxiv cs.lg decision evolution expert experts generated history however imitation learning makers making modeling online learning process recommender systems streaming systems type