TY  - CONF
ID  - www200926
UR  - http://www2009.eprints.org/26/
A1  - Ghosh, Arpita
A1  - Rubinstein, Benjamin I. P.
A1  - Vassilvitskii, Sergei
A1  - Zinkevich, Martin
Y1  - 2009/04//
N2  - Motivated by the emergence of auction-based marketplaces for display ads such as the Right Media Exchange, we study the design of a bidding agent that implements a display advertising campaign by bidding in such a marketplace. The bidding agent must acquire a given number of impressions with a given target spend, when the highest external bid in the marketplace is drawn from an unknown distribution P . The quantity and spend constraints arise from the fact that display ads are usually sold on a CPM basis. We consider both the full information setting, where the winning price in each auction is announced publicly, and the partially observable setting where only the winner obtains information about the distribution; these differ in the penalty incurred by the agent while attempting to learn the distribution. We provide algorithms for both settings, and prove performance guarantees using bounds on uniform closeness from statistics, and techniques from online learning. We experimentally evaluate these algorithms: both algorithms perform very well with respect to both target quantity and spend; further, our algorithm for the partially observable case performs nearly as well as that for the fully observable setting despite the higher penalty incurred during learning.
TI  - Adaptive Bidding for Display Advertising
SP  - 251
M2  - Madrid, Spain
AV  - public
EP  - 251
T2  - 18th International World Wide Web Conference
ER  -