A reinforcement learning method to select ad networks in Waterfall Strategy

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55 Downloads (Pure)

Abstract

A high percentage of online advertising is currently performed through real time bidding. Impressions are generated once a user visits the websites containing empty ad slots, which are subsequently sold in an online ad exchange market. Nowadays, one of the most important sources of income for publishers who own websites is through online advertising. From a publisher’s point of view it is critical to send its impressions to most profitable ad networks and to fill its ad slots quickly in order to increase their revenue. In this paper we present a method for helping publishers to decide which ad networks to use for each available impression. Our proposed method uses reinforcement learning with initial state-action values obtained from a prediction model to find the best ordering of ad networks in the waterfall fashion. We show that this method increases the expected revenue of the publisher.
Original languageEnglish
Title of host publicationICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence
EditorsJaap van den Herik, Luc Steels, Ana Rocha
PublisherSCITEPRESS-Science and Technology Publications, Lda.
Pages256-265
Number of pages10
Volume2
ISBN (Electronic)9789897583506
ISBN (Print)978-989-758-350-6
DOIs
Publication statusPublished - 2019
Event11th International Conference on Agents and Artificial Intelligence, ICAART 2019 - Prague, Czech Republic
Duration: 19 Feb 201921 Feb 2019
http://www.icaart.org/

Conference

Conference11th International Conference on Agents and Artificial Intelligence, ICAART 2019
Abbreviated titleICAART2019
CountryCzech Republic
CityPrague
Period19/02/1921/02/19
Internet address

Keywords

  • Ad Network
  • Online AD Auction
  • Predictive Model
  • Real Time Bidding
  • Reinforcement Learning
  • Supply Side Platform

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