A Map of Bandits for E-commerce
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
- 2019Introduction to Multi-Armed Banditscited by 475
- 2018Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Samplingcited by 166
- 2013Bandits with Knapsackscited by 163
- 2013Online Learning under Delayed Feedbackcited by 137
- 2016Off-policy evaluation for slate recommendationcited by 102
- 2014Best-Arm Identification in Linear Banditscited by 98
- 2018Linear Bandits with Stochastic Delayed Feedbackcited by 55
- 2019A Survey on Practical Applications of Multi-Armed and Contextual Banditscited by 44
- 2019Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adaptingcited by 42
- 2014Efficient Learning in Large-Scale Combinatorial Semi-Banditscited by 36
- 2018An Efficient Bandit Algorithm for Realtime Multivariate Optimizationcited by 34
- 2019Best Arm Identification in Generalized Linear Banditscited by 18
4 more
- 2019Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matchingcited by 16
- 2020Learning to Rank in the Position Based Model with Bandit Feedbackcited by 10
- 2018Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplacecited by 3
- 2019A Bandit Approach to Posterior Dialog Orchestration Under a Budgetcited by 3
Built on by
Citing papers per year: 2022: 3; 2023: 1; 2024: 2; 2025: 2; 2026: 1.
9 papers.
- 2022Reward Reports for Reinforcement Learningcited by 18
- 2023Practical Bandits: An Industry Perspectivecited by 9
- 2024DISCO: An End-to-End Bandit Framework for Personalised Discount Allocationcited by 3
- 2025Optimal Arm Elimination Algorithms for Combinatorial Banditscited by 3
- 2022Extending Open Bandit Pipeline to Simulate Industry Challengescited by 2
- 2024The Digital Transformation in Health: How AI Can Improve the Performance of Health Systemscited by 1
- 2025Adversarial Combinatorial Semi-bandits with Graph Feedbackcited by 1
- 2022A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routingcited by 0
- 2026Design and Evaluation of Whole-Page Experience Optimization for E-commerce Searchcited by 0
- 2026Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search23 Jan 2026
- 2025Optimal Arm Elimination Algorithms for Combinatorial Bandits28 Oct 2025
- 2025Adversarial Combinatorial Semi-bandits with Graph Feedback26 Feb 2025
- 2024The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems24 Sep 2024
- 2024DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation10 Jun 2024
- 2023Practical Bandits: An Industry Perspective2 Feb 2023
- 2022Extending Open Bandit Pipeline to Simulate Industry Challenges9 Sep 2022
- 2022A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing8 Sep 2022
- 2022Reward Reports for Reinforcement Learning22 Apr 2022