A Batched Multi-Armed Bandit Approach to News Headline Testing
Abstract
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.
Cite
@article{arxiv.1908.06256,
title = {A Batched Multi-Armed Bandit Approach to News Headline Testing},
author = {Yizhi Mao and Miao Chen and Abhinav Wagle and Junwei Pan and Michael Natkovich and Don Matheson},
journal= {arXiv preprint arXiv:1908.06256},
year = {2019}
}
Comments
IEEE BigData, 2018