English

A Map of Bandits for E-commerce

Machine Learning 2021-07-05 v1

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.

Keywords

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/

R2 v1 2026-06-24T03:49:14.181Z