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dc.creatorSilva, Jesús
dc.creatorVarela Izquierdo, Noel
dc.creatorCabrera, Danelys
dc.creatorPineda, Omar
dc.date.accessioned2020-11-12T17:37:13Z
dc.date.available2020-11-12T17:37:13Z
dc.date.issued2020
dc.identifier.issn2194-5357
dc.identifier.urihttps://hdl.handle.net/11323/7280
dc.description.abstractFor advertising networks to increase their revenues, priority must be given to the most profitable ads. The most important factor in the profitability of an ad is the click-through-rate (CTR) which is the probability that a user will click on the ad on a Web page. To predict the CTR, a number of supervised rating models have been trained and their performance is compared to artificial organic networks (AON). The conclusion is that these networks are a good solution to predict the CTR of an ad.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectArtificial organic networks in advertisingspa
dc.subjectCPC advertising networksspa
dc.subjectCTR predictionspa
dc.subjectSupervised rating modelsspa
dc.titleCTR prediction of internet ads using artificial organic networksspa
dc.typePreprintspa
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dc.type.hasVersioninfo:eu-repo/semantics/draftspa
dc.source.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090094053&doi=10.1007%2f978-981-15-6876-3_38&partnerID=40&md5=7083b813245ab891a09c08f2e320d3f6spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.date.embargoEnd2021-01-31


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