MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm

Wei Zhang, Brendan Kitts, Yanjun Han, Zhengyuan Zhou, Tingyu Mao, Hao He, Shengjun Pan, Aaron Flores, San Gultekin, Tsachy Weissman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bid Shading has become increasingly important in Online Advertising, with a large amount of commercial [4,12,13,29] and research work [11,20,28] recently published. Most approaches for solving the bid shading problem involve estimating the probability of win distribution, and then maximizing surplus [28]. These generally use parametric assumptions for the distribution, and there has been some discussion as to whether Log-Normal, Gamma, Beta, or other distributions are most effective [8,38,41,44]. In this paper, we show evidence that online auctions generally diverge in interesting ways from classic distributions. In particular, real auctions generally exhibit significant structure, due to the way that humans set up campaigns and inventory floor prices [16,26]. Using these insights, we present a nonparametric method for Bid Shading which enables the exploitation of this deep structure. The algorithm has low time and space complexity, and is designed to operate within the challenging millisecond Service Level Agreements of Real-Time Bid Servers. We deploy it in one of the largest Demand Side Platforms in the United States, and show that it reliably out-performs best in class Parametric benchmarks. We conclude by suggesting some ways that the best aspects of parametric and nonparametric approaches could be combined.

Original languageEnglish (US)
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages3928-3936
Number of pages9
ISBN (Electronic)9781450383325
DOIs
StatePublished - Aug 14 2021
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: Aug 14 2021Aug 18 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/TerritorySingapore
CityVirtual, Online
Period8/14/218/18/21

Keywords

  • advertising
  • auction
  • bid
  • online bidding
  • optimization
  • shading

ASJC Scopus subject areas

  • Software
  • Information Systems

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