  We propose a contextual bandit based model to capture the learning and social
welfare goals of a web platform in the presence of myopic users. By using
payments to incentivize these agents to explore different
items/recommendations, we show how the platform can learn the inherent
attributes of items and achieve a sublinear regret while maximizing cumulative
social welfare. We also calculate theoretical bounds on the cumulative costs of
incentivization to the platform. Unlike previous works in this domain, we
consider contexts to be completely adversarial, and the behavior of the
adversary is unknown to the platform. Our approach can improve various
engagement metrics of users on e-commerce stores, recommendation engines and
matching platforms.
