Ad auctions with data

Hu Fu*, Patrick Jordan, Mohammad Mahdian, Uri Nadav, Inbal Talgam-Cohen, Sergei Vassilvitskii

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

The holy grail of online advertising is to target users with ads matched to their needs with such precision that the users respond to the ads, thereby increasing both advertisers' and users' value. The current approach to this challenge utilizes information about the users: their gender, their location, the websites they have visited before, and so on. Incorporating this data in ad auctions poses an economic challenge: can this be done in a way that the auctioneer's revenue does not decrease (at least on average)? This is the problem we study in this paper. Our main result is that in Myerson's optimal mechanism, for a general model of data in auctions, additional data leads to additional expected revenue. In the context of ad auctions we show that for the simple and common mechanisms, namely second price auction with reserve prices, there are instances in which additional data decreases the expected revenue, but this decrease is by at most a small constant factor under a standard regularity assumption.

Original languageEnglish
Title of host publicationAlgorithmic Game Theory - 5th International Symposium, SAGT 2012, Proceedings
Pages168-179
Number of pages12
DOIs
StatePublished - 2012
Externally publishedYes
Event5th International Symposium on Algorithmic Game Theory, SAGT 2012 - Barcelona, Spain
Duration: 22 Oct 201223 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7615 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Algorithmic Game Theory, SAGT 2012
Country/TerritorySpain
CityBarcelona
Period22/10/1223/10/12

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