Fast Core Pricing for Rich Advertising Auctions

TL;DR

This paper introduces a fast algorithm for core pricing in complex ad auctions, significantly increasing revenue and enabling practical real-time implementation in online advertising.

Contribution

It presents a novel, efficient combinatorial algorithm for approximate bidder optimal core points with theoretical guarantees, improving revenue in rich ad auction settings.

Findings

Core pricing yields 26% more revenue than VCG.

Algorithm is faster than previous heuristics.

Core pricing approaches GSP revenue levels.

Abstract

Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey-Clarke-Groves (VCG) can yield unacceptably low revenue. We therefore study core selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Our main result is a combinatorial algorithm that finds an approximate bidder optimal core point with almost linear number of calls to the welfare maximization oracle. Our algorithm is faster than previously-proposed heuristics in the literature and has theoretical guarantees. We conclude that core pricing is implementable even for very time sensitive practical use cases such as realtime auctions for online advertising and can yield more revenue. We justify this claim experimentally using the Microsoft Bing Ad Auction data, through which we show our core pricing algorithm generates almost 26% more revenue than VCG on average, about 9% more revenue than other core pricing rules known in the literature, and almost matches the revenue of the standard Generalized Second Price (GSP) auction.