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Facing growing competition from online rivals, the retail industry isincreasingly investing in their online shopping platforms to win the high-stakebattle of customer' loyalty. User experience is playing an essential role inthis competition, and retailers are continuously experimenting and optimizingtheir user interface for better user experience. The cost of experimentation isdominated by the opportunity cost of providing a suboptimal service to thecustomers. Through this paper, we demonstrate the effectiveness ofopportunistic bandits to make the experiments as inexpensive as possible usingreal online retail data. In fact, we model user interface experimentation as anopportunistic bandit problem, in which the cost of exploration varies under afactor extracted from customer features. We achieve significant regretreduction by mitigating costly exploration and providing extra contextualinformation that helps to guide the testing process. Moreover, we analyze theadvantages and challenges of using opportunistic bandits for online retailexperimentation.

An Opportunistic Bandit Approach for User Interface Experimentation