A Survey on Practical Applications of Multi-Armed and Contextual Bandits

TL;DR

This survey reviews recent practical applications of multi-armed and contextual bandits across diverse domains, highlighting new developments, trends, and future directions in this rapidly evolving field.

Contribution

It provides a comprehensive taxonomy and summary of state-of-the-art methods for real-world multi-armed bandit applications, along with insights into current trends and future perspectives.

Findings

Taxonomy of MAB applications across domains

Summary of recent algorithms and methods

Identification of emerging trends and future directions

Abstract

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.