A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
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abstract
We present a new strategy for gap estimation in randomized algorithms for multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the stochastic regime the strategy reduces dependence of regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an additive factor of order $\Delta e^{1/\Delta^2}$, where $\Delta$ is the minimal gap of a problem instance. In the adversarial regime regret guarantee remains unchanged.
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Accelerated learning from recommender systems using multi-armed bandit
A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.