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Inventory allocation for online graphical display advertising using multi-objective optimization

  • Jian Yang
  • , Erik Vee
  • , Sergei Vassilvitskii
  • , John Tomlin
  • , Jayavel Shanmugasundaram
  • , Tasos Anastasakos
  • , Oliver Kennedy
  • Yahoo Research Labs
  • Marketshare LLC
  • Alphabet Inc.

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

6 Scopus citations

Abstract

We discuss a multi-objective/goal programming model for the allocation of inventory of graphical advertisements. The model considers two types of campaigns: guaranteed delivery (GD), which are sold months in advance, and non-guaranteed delivery (NGD), which are sold using real-time auctions. We investigate various advertiser and publisher objectives such as (a) revenue from the sale of user visits, clicks and conversions, (b) future revenue from the sale of NGD inventory, and (c) "fairness" of allocation. While the first two objectives are monetary, the third is not. This combination of demand types and objectives leads to potentially many variations of our model, which we delineate and evaluate. Our experimental results, which are based on optimization runs using real data sets, demonstrate the effectiveness and flexibility of the proposed model.

Original languageEnglish
Title of host publicationICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems
Pages293-304
Number of pages12
StatePublished - 2012
Event1st International Conference on Operations Research and Enterprise Systems, ICORES 2012 - Vilamoura, Algarve, Portugal
Duration: Feb 4 2012Feb 6 2012

Publication series

NameICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems

Conference

Conference1st International Conference on Operations Research and Enterprise Systems, ICORES 2012
Country/TerritoryPortugal
CityVilamoura, Algarve
Period02/4/1202/6/12

Keywords

  • Advertising
  • Inventory allocation
  • Multi-objective optimization

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