# Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments

arXiv: https://arxiv.org/abs/1907.02178 · HTML: https://mlsanity.com/paper/1907.02178/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Evaluating Online Bandit Exploration In Large-Scale Recommender System](https://mlsanity.com/paper/2304.02572/) (2023)
- [Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms](https://mlsanity.com/paper/2210.00340/) (2022)
- [Adaptive Experimentation with Delayed Binary Feedback](https://mlsanity.com/paper/2202.00846/) (2022)
- [Advertising Media and Target Audience Optimization via High-dimensional Bandits](https://mlsanity.com/paper/2209.08403/) (2022)
- [Adapting Bandit Algorithms for Settings with Sequentially Available Arms](https://mlsanity.com/paper/2109.15228/) (2021)
- [Few-for-Many Best-Arm Identification in Multi-Objective Bandits](https://mlsanity.com/paper/2610.16840/) (2026)

## Abstract (arXiv)

Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used "split-testing" strategies that have not explicitly addressed the complexities induced by targeted audiences that can potentially overlap with one another. This paper presents an adaptive algorithm that addresses the problem via online experimentation. The algorithm is set up as a contextual bandit and addresses the overlap issue by partitioning the target audiences into disjoint, non-overlapping sub-populations. It learns an optimal creative display policy in the disjoint space, while assessing in parallel which creative has the best match in the space of possibly overlapping target audiences. Experiments show that the proposed method is more efficient compared to naive "split-testing" or non-adaptive "A/B/n" testing based methods. We also describe a testing product we built that uses the algorithm. The product is currently deployed on the advertising platform of JD.com, an eCommerce company and a publisher of digital ads in China.