Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve

TL;DR

This paper develops a theoretical framework and algorithms for optimizing reserve prices in second-price auctions to maximize revenue, supported by experiments on synthetic and real data.

Contribution

It introduces a new learning-based approach for reserve price optimization and provides a comprehensive theoretical analysis of the associated loss function.

Findings

Algorithms effectively optimize reserve prices in practice

Theoretical analysis reveals complex properties of the revenue loss function

Experimental results demonstrate improved revenue performance

Abstract

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.