CTR prediction for optimizing the negotiation of internet advertising campaigns

dc.creatorSilva, Jesús
dc.creatorVargas, Jesús
dc.creatorRizzo-Vergara, Dawin
dc.creatorAraya Ugarte, Guillermo Agustín
dc.creatorRosado, César Enrique
dc.creatorPineda, Omar
dc.creatorQuintero, Benjamín
dc.date2021-01-21T13:38:32Z
dc.date2021-01-21T13:38:32Z
dc.date2020
dc.date.accessioned2023-10-03T19:08:38Z
dc.date.available2023-10-03T19:08:38Z
dc.identifierhttps://hdl.handle.net/11323/7739
dc.identifierhttps://doi.org/10.1007/978-981-15-4875-8_13
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168171
dc.descriptionWeb advertising campaigns have the particularity that allow to measure the performance of campaigns based on different metrics, among which are the cost per thousand impressions (CPM-Cost Per mille), cost per click (CPC) and the click-to-print ratio (CTR-Click Through Ratio). For this reason, each ad has a specific objective based on these indicators which aim to distribute the purchase of advertising space on the Internet in the best possible way in order to have a better return on investment based on these metrics. The costs incurred in the development of its services is significant and the objectives of the campaigns are not always achieved because it assumes the variability of Internet user behavior. This project consists of proposing a regression model based on the historical data of the companies providing the programmatic purchasing service, in order to optimize negotiations on performance metrics in advertising campaigns with advertisers.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceSmart Innovation, Systems and Technologies
dc.sourcehttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_13
dc.subjectSmart cities
dc.subjectWireless sensor networks
dc.subjectInternet of things
dc.subjectWireless nodes
dc.subjectCommunication architecture
dc.titleCTR prediction for optimizing the negotiation of internet advertising campaigns
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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