  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.
