Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
This work addresses revenue optimization for search engines and online sites, but it appears incremental as it builds on existing auction theory with new algorithms.
The authors tackled the problem of selecting reserve prices to optimize revenue in second-price auctions, presenting a theoretical analysis and novel algorithms that demonstrated effectiveness in experiments on synthetic and real data.
Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real data demonstrating their effectiveness.