Dynamic contextual multi Arm bandits in display advertisement

We model the ad selection task as a multi-Armed bandit problem. Standard assumptions in the multi-Armed bandit (MAB) setting are that samples drawn from each arm are independent and identically distributed, rewards (or conversion rates in our scenario) are stationary and rewards feedback are immediate. Although the payoff function of an arm is allowed to evolve over time, the evolution is assumed to be slow. Display ads, on the other hand, are regularly created while others are removed from circulation. This can occur when budgets run out, campaign goal changes, holiday season ends and many other latent factors that go beyond the control of the ad selection system. Another big challenge is that the set of available ads is often extremely huge but standard multi-Armed bandit strategies converge with linear time complexity that cannot accommodate the usually dynamic changes. Due to the above challenges and the restrictions of the original MAB, we propose a novel dynamic contextual MAB which tightly integrates components of dynamic conversion rates prediction, contextual learning and arm overlapping modeling in a principled framework. Besides we propose an accompanied meta analyses framework that allows us to conclude experiments in a more statistically robust manner. We demonstrate on a world leading demand side platform (DSP) that our framework can effectively discriminate premium arms and significantly outperform some standard variations of MAB to these settings.