A Survey on Practical Applications of Multi-Armed and Contextual Bandits
It is a survey paper that synthesizes existing knowledge for researchers and practitioners, offering no new methods or results.
This paper provides a comprehensive review of recent developments in multi-armed and contextual bandit applications across domains like recommender systems and healthcare, summarizing state-of-the-art methods and identifying future trends.
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.