A Map of Bandits for E-commerce
This work addresses a gap for practitioners in e-commerce by providing a focused mapping tool, though it is incremental as it builds on existing surveys and textbooks without introducing new algorithms.
The paper tackles the problem of practitioners struggling to select appropriate Bandit algorithms for e-commerce applications by creating a structured map that focuses on key decision points like reward, action, and features, resulting in a practical guide to navigate the diverse Bandit literature.
The rich body of Bandit literature not only offers a diverse toolbox of algorithms, but also makes it hard for a practitioner to find the right solution to solve the problem at hand. Typical textbooks on Bandits focus on designing and analyzing algorithms, and surveys on applications often present a list of individual applications. While these are valuable resources, there exists a gap in mapping applications to appropriate Bandit algorithms. In this paper, we aim to reduce this gap with a structured map of Bandits to help practitioners navigate to find relevant and practical Bandit algorithms. Instead of providing a comprehensive overview, we focus on a small number of key decision points related to reward, action, and features, which often affect how Bandit algorithms are chosen in practice.