  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.
