mlsanityData to 7 Oct 2026

A Map of Bandits for E-commerce

arXiv 2107.00680 ↗First posted 1 Jul 2021cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 52 references that are in the map. First posted on arXiv 1 Jul 2021.

Builds on

16 of 52 references.

References · 16

  1. 2019Introduction to Multi-Armed Banditscited by 475
  2. 2018Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Samplingcited by 166
  3. 2013Bandits with Knapsackscited by 163
  4. 2013Online Learning under Delayed Feedbackcited by 137
  5. 2016Off-policy evaluation for slate recommendationcited by 102
  6. 2014Best-Arm Identification in Linear Banditscited by 98
  7. 2018Linear Bandits with Stochastic Delayed Feedbackcited by 55
  8. 2019A Survey on Practical Applications of Multi-Armed and Contextual Banditscited by 44
  9. 2019Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adaptingcited by 42
  10. 2014Efficient Learning in Large-Scale Combinatorial Semi-Banditscited by 36
  11. 2018An Efficient Bandit Algorithm for Realtime Multivariate Optimizationcited by 34
  12. 2019Best Arm Identification in Generalized Linear Banditscited by 18
4 more
  1. 2019Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matchingcited by 16
  2. 2020Learning to Rank in the Position Based Model with Bandit Feedbackcited by 10
  3. 2018Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplacecited by 3
  4. 2019A Bandit Approach to Posterior Dialog Orchestration Under a Budgetcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

9 papers.