A Map of Bandits for E-commerce

TL;DR

This paper creates a structured map linking various Bandit algorithms to practical e-commerce applications, helping practitioners select suitable methods based on key decision points like reward, action, and features.

Contribution

It introduces a structured mapping framework that connects Bandit algorithms to real-world e-commerce problems, addressing the gap in application guidance.

Findings

Provides a decision map for Bandit algorithm selection

Highlights key decision points influencing algorithm choice

Facilitates practical application of Bandits in e-commerce

Abstract

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.