Abstract
Click Through Rate (CTR) prediction is crucial in digital advertising for optimizing marketing strategies. This paper presents a review of significant contributions in this field, highlighting methodologies and findings from various studies. Pioneering research laid foundational groundwork for CTR estimation methods, while subsequent analyses explored the impact of ad types and design effects on user engagement. Utilization of data mining techniques and the proposal of advanced prediction models further enhanced CTR prediction accuracy. Additionally, this paper introduces our method utilizing XGBoost, a powerful ensemble learning algorithm, to address existing challenges and enhance CTR prediction accuracy. This review offers valuable insights for marketers aiming to optimize their advertising campaigns in the dynamic landscape of advertising.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Theory and Practice of Engineering Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
