A decision support method to increase the revenue of ad publishers in waterfall strategy

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Abstract

Online advertising is one of the most important sources of income for many online publishers. The process is as easy as placing slots in the website and selling those slots in real time bidding auctions. Since websites load in few milliseconds, the bidding and selling process should not take too much time. Sellers or publishers of advertisements tend to maximize the revenue obtained through online advertising. Recently, we proposed a method to select the most profitable ad network for each ad request. Our method consists of two parts: a prediction model and a reinforcement learning modeling. In this paper we evaluate the prediction model and test two methods of ad network ordering. First we use prediction model and second the two-step method. We show that the ad network ordering obtained from the second step of our method increases the revenue and the prediction model provides initial values which could not be used as a decision method.
Original languageEnglish
Title of host publicationCIFEr 2019 - IEEE Conference on Computational Intelligence for Financial Engineering and Economics
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728100333
DOIs
Publication statusPublished - 1 May 2019
Event2019 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, (CIFEr2019) - Shenzhen, China
Duration: 4 May 20195 May 2019
http://www.ieee-cifer.org/website_cifer/about_2019.html

Conference

Conference2019 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, (CIFEr2019)
Abbreviated titleCIFEr2019
CountryChina
CityShenzhen
Period4/05/195/05/19
Internet address

Keywords

  • ad networks
  • Predictive model
  • Real time bidding
  • Reinforcement Learning

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