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Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Suleymanzade, Suleyman | - |
dc.date.accessioned | 2024-03-22T18:42:27Z | - |
dc.date.available | 2024-03-22T18:42:27Z | - |
dc.date.issued | 2023-12-01 | - |
dc.identifier.issn | 2616-6127 2617-4383 | - |
dc.identifier.uri | http://dspace.azjhpc.org/xmlui/handle/123456789/268 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.publisher | Azerbaijan Journal of High Performance Computing | en_US |
dc.subject | Data Splitting | en_US |
dc.subject | CTR-related | en_US |
dc.subject | XGBoost | en_US |
dc.subject | CTR Prediction | en_US |
dc.title | Predictive Modeling of Click-Through Rates: A Regression Analysis Approach | en_US |
dc.type | Article | en_US |
dc.source.journaltitle | Azerbaijan Journal of High Performance Computing | en_US |
dc.source.volume | 6 | en_US |
dc.source.issue | 2 | en_US |
dc.source.beginpage | 199 | en_US |
dc.source.endpage | 202 | en_US |
dc.source.numberofpages | 4 | en_US |
Appears in Collections: | Azerbaijan Journal of High Performance Computing |
Files in This Item:
File | Description | Size | Format | |
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doi.org.10.32010.26166127.2023.6.2.199.202.pdf | 567.66 kB | Adobe PDF | View/Open |
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