Learning to Clear the Market
This work addresses revenue optimization in auctions and markets, particularly for ad exchanges, but appears incremental as it builds on existing learning and economic theory.
The authors tackled the problem of market clearing by predicting clearing prices using a learning framework to optimize revenue in auctions with contextual information, achieving superior revenue and efficiency trade-offs compared to existing methods on a large dataset of display ad bids.
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to perform revenue optimization in auctions and markets with contextual information. The economic intuition behind market clearing allows us to obtain fine-grained control over the aggressiveness of the resulting pricing policy, grounded in theory. To evaluate our approach, we fit a model of clearing prices over a massive dataset of bids in display ad auctions from a major ad exchange. The learned prices outperform other modeling techniques in the literature in terms of revenue and efficiency trade-offs. Because of the convex nature of the clearing loss function, the convergence rate of our method is as fast as linear regression.