Via Zoom: Incentive-Aware Contextual Pricing with Non-Parametric Market Noise

Motivated by the availability of the massive amount of real-time data in online advertising markets, we study a dynamic pricing problem for repeated contextual second-price auctions with strategic buyers whose goals are to maximize their long-term time discounted utility. The seller has very limited information about buyers’ overall demand curves, which depends on d-dimensional context vectors characterizing auctioned items, and a non-parametric market noise distribution that captures buyers’ idiosyncratic tastes. The noise distribution, as well as the relationship between the context vectors and buyers’ demand curves, are both unknown to the seller. We focus on designing the seller’s learning policy to set contextual reserve prices where the seller’s goal is to minimize his regret for revenue, and further show theoretical performance guarantees. To the best of our knowledge, our proposed policy achieves the lowest regret in a contextual, non-parametric, and strategic environment for the dynamic pricing problem in repeated second-price auctions.