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.
Work done while the authors were at Yahoo! Reseaerch
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Fu, H., Jordan, P., Mahdian, M., Nadav, U., Talgam-Cohen, I., Vassilvitskii, S. (2012). Ad Auctions with Data. In: Serna, M. (eds) Algorithmic Game Theory. SAGT 2012. Lecture Notes in Computer Science, vol 7615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33996-7_15
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DOI: https://doi.org/10.1007/978-3-642-33996-7_15
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