Improving CTR Prediction in Advertising with XGBoost - Wu, Yingyi (Journal of Theory and Practice of Engineering Science, 2024)
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.