Google Research

Multi-Score Position Auctions

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM 2016

Abstract

In this paper we propose a general family of position auctions used in paid search, which we call multi-score position auctions. These auctions contain the GSP auction and the GSP auction with squashing as special cases. We show experimentally that these auctions contain special cases that perform better than the GSP auction with squashing, in terms of revenue, and the number of clicks on ads. In particular, we study in detail the special case that squashes the first slot alone and show that this beats pure squashing (which squashes all slots uniformly). We study the equilibria that arise in this special case to examine both the first order and the second order effect of moving from the squashing-all-slots auction to the squash-only-the-top-slot auction. For studying the second order effect, we simulate auctions using the value-relevance correlated distribution suggested in Lahaie and Pennock [2007]. Since this distribution is derived from a study of value and relevance distributions in Yahoo! we believe the insights derived from this simulation to be valuable. For measuring the first order effect, in addition to the said simulation, we also conduct experiments using auction data from Bing over several weeks that includes a random sample of all auctions.

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