Please use this identifier to cite or link to this item: http://dspace.azjhpc.org/xmlui/handle/123456789/268
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dc.contributor.authorSuleymanzade, Suleyman-
dc.date.accessioned2024-03-22T18:42:27Z-
dc.date.available2024-03-22T18:42:27Z-
dc.date.issued2023-12-01-
dc.identifier.issn2616-6127 2617-4383-
dc.identifier.urihttp://dspace.azjhpc.org/xmlui/handle/123456789/268-
dc.description.abstractThis research uses advanced regression techniques to develop a robust predictive model for Click-Through Rates (CTR) in online advertising. The study leverages a diverse dataset encompassing various advertising campaigns and user interactions to uncover patterns and relationships influencing click-through behavior. The goal is to provide advertisers with a tool for accurate CTR prediction, enabling them to optimize campaigns and allocate resources effectively.en_US
dc.language.isoenen_US
dc.publisherAzerbaijan Journal of High Performance Computingen_US
dc.subjectData Splittingen_US
dc.subjectCTR-relateden_US
dc.subjectXGBoosten_US
dc.subjectCTR Predictionen_US
dc.titlePredictive Modeling of Click-Through Rates: A Regression Analysis Approachen_US
dc.typeArticleen_US
dc.source.journaltitleAzerbaijan Journal of High Performance Computingen_US
dc.source.volume6en_US
dc.source.issue2en_US
dc.source.beginpage199en_US
dc.source.endpage202en_US
dc.source.numberofpages4en_US
Appears in Collections:Azerbaijan Journal of High Performance Computing

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