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Optimizing news headlines is important for publishers and media sites. Acompelling headline will increase readership, user engagement and socialshares. At Yahoo Front Page, headline testing is carried out using atest-rollout strategy: we first allocate equal proportion of the traffic toeach headline variation for a defined testing period, and then shift all futuretraffic to the best-performing variation. In this paper, we introduce amulti-armed bandit (MAB) approach with batched Thompson Sampling (bTS) todynamically test headlines for news articles. This method is able to graduallyallocate traffic towards optimal headlines while testing. We evaluate the bTSmethod based on empirical impressions/clicks data and simulated user responses.The result shows that the bTS method is robust, converges accurately andquickly to the optimal headline, and outperforms the test-rollout strategy by3.69% in terms of clicks.

A Batched Multi-Armed Bandit Approach to News Headline Testing