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This paper describes an engine to optimize web publisher revenues fromsecond-price auctions. These auctions are widely used to sell online ad spacesin a mechanism called real-time bidding (RTB). Optimization within theseauctions is crucial for web publishers, because setting appropriate reserveprices can significantly increase revenue. We consider a practical real-worldsetting where the only available information before an auction occurs consistsof a user identifier and an ad placement identifier. The real-world challengeswe had to tackle consist mainly of tracking the dependencies on both the userand placement in an highly non-stationary environment and of dealing withcensored bid observations. These challenges led us to make the following designchoices: (i) we adopted a relatively simple non-parametric regression model ofauction revenue based on an incremental time-weighted matrix factorizationwhich implicitly builds adaptive users' and placements' profiles; (ii) wejointly used a non-parametric model to estimate the first and second bids'distribution when they are censored, based on an on-line extension of theAalen's Additive model.  Our engine is a component of a deployed system handling hundreds of webpublishers across the world, serving billions of ads a day to hundreds ofmillions of visitors. The engine is able to predict, for each auction, anoptimal reserve price in approximately one millisecond and yields a significantrevenue increase for the web publishers.

Real-Time Optimization Of Web Publisher RTB Revenues