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Bandit algorithms are often used in the e-commerce industry to train MachineLearning (ML) systems when pre-labeled data is unavailable. However, theindustry setting poses various challenges that make implementing banditalgorithms in practice non-trivial. In this paper, we elaborate on thechallenges of off-policy optimisation, delayed reward, concept drift, rewarddesign, and business rules constraints that practitioners at Booking.comencounter when applying bandit algorithms. Our main contributions is anextension to the Open Bandit Pipeline (OBP) framework. We provide simulationcomponents for some of the above-mentioned challenges to provide futurepractitioners, researchers, and educators with a resource to address challengesencountered in the e-commerce industry.

Extending Open Bandit Pipeline to Simulate Industry Challenges