Real-Time Bidding with Side Information
Name
7101-real-time-bidding-with-side-information.pdf
Description
Published version
Size
279.75 KB
Format
Adobe PDF
Checksum (MD5)
5d742c361e1e930304fa4e578bf322c4
Author(s)
Flajolet, Arthur
Jaillet, Patrick
Date Issued
2017
Citation
Flajolet, Arthur and Jaillet, Patrick. 2017. "Real-Time Bidding with Side Information."
Version
Final published version
Abstract
© 2017 Neural information processing systems foundation. All rights reserved. We consider the problem of repeated bidding in online advertising auctions when some side information (e.g. browser cookies) is available ahead of submitting a bid in the form of a d-dimensional vector. The goal for the advertiser is to maximize the total utility (e.g. the total number of clicks) derived from displaying ads given that a limited budget B is allocated for a given time horizon T. Optimizing the bids is modeled as a contextual Multi-Armed Bandit (MAB) problem with a knapsack constraint and a continuum of arms. We develop UCB-type algorithms that combine two streams of literature: the confidence-set approach to linear contextual MABs and the probabilistic bisection search method for stochastic root-finding. Under mild assumptions on the underlying unknown distribution, we establish distribution-independent regret bounds of order Õ(d · √T) when either B = ∞ or when B scales linearly with T.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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