A reinforcement learning method to select ad networks in Waterfall Strategy

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11 Citaten (Scopus)
95 Downloads (Pure)

Samenvatting

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.
Originele taal-2Engels
TitelICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence
RedacteurenJaap van den Herik, Luc Steels, Ana Rocha
UitgeverijSciTePress Digital Library
Pagina's256-265
Aantal pagina's10
Volume2
ISBN van elektronische versie9789897583506
ISBN van geprinte versie978-989-758-350-6
DOI's
StatusGepubliceerd - 2019
Evenement11th International Conference on Agents and Artificial Intelligence, ICAART 2019 - Prague, Tsjechië
Duur: 19 feb. 201921 feb. 2019
http://www.icaart.org/

Congres

Congres11th International Conference on Agents and Artificial Intelligence, ICAART 2019
Verkorte titelICAART2019
Land/RegioTsjechië
StadPrague
Periode19/02/1921/02/19
Internet adres

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