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dc.creatorSilva, Jesús
dc.creatorVarela Izquierdo, Noel
dc.creatorCabrera, Danelys
dc.creatorPineda, Omar
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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
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
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