A Map of Bandits for 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.
Cite
@article{arxiv.2107.00680,
title = {A Map of Bandits for E-commerce},
author = {Yi Liu and Lihong Li},
journal= {arXiv preprint arXiv:2107.00680},
year = {2021}
}
Comments
Accepted by KDD Bandit and RL workshop: https://sites.google.com/view/marble-kdd/