  In this paper, we study the stochastic multi-armed bandit problem, where the
reward is driven by an unknown random variable. We propose a new variant of the
Upper Confidence Bound (UCB) algorithm called Hellinger-UCB, which leverages
the squared Hellinger distance to build the upper confidence bound. We prove
that the Hellinger-UCB reaches the theoretical lower bound. We also show that
the Hellinger-UCB has a solid statistical interpretation. We show that
Hellinger-UCB is effective in finite time horizons with numerical experiments
between Hellinger-UCB and other variants of the UCB algorithm. As a real-world
example, we apply the Hellinger-UCB algorithm to solve the cold-start problem
for a content recommender system of a financial app. With reasonable
assumption, the Hellinger-UCB algorithm has a convenient but important lower
latency feature. The online experiment also illustrates that the Hellinger-UCB
outperforms both KL-UCB and UCB1 in the sense of a higher click-through rate
(CTR).
