# A Batched Multi-Armed Bandit Approach to News Headline Testing

arXiv: https://arxiv.org/abs/1908.06256 · HTML: https://mlsanity.com/paper/1908.06256/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Batched Nonparametric Bandits via k-Nearest Neighbor UCB](https://mlsanity.com/paper/2505.10498/) (2025)
- [A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce](https://mlsanity.com/paper/2311.10782/) (2023)
- [Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search](https://mlsanity.com/paper/2602.02514/) (2026)
- [On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits](https://mlsanity.com/paper/2605.25789/) (2026)

## Abstract (arXiv)

Optimizing news headlines is important for publishers and media sites. A compelling headline will increase readership, user engagement and social shares. At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a defined testing period, and then shift all future traffic to the best-performing variation. In this paper, we introduce a multi-armed bandit (MAB) approach with batched Thompson Sampling (bTS) to dynamically test headlines for news articles. This method is able to gradually allocate traffic towards optimal headlines while testing. We evaluate the bTS method based on empirical impressions/clicks data and simulated user responses. The result shows that the bTS method is robust, converges accurately and quickly to the optimal headline, and outperforms the test-rollout strategy by 3.69% in terms of clicks.