Year: 2025
Type: article
Abstract: This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning problem that has been extensively studied over the past several years. As digital marketing continues to grow rapidly into a multi-billion-dollar industry, this study aims to find the most effective machine learning model to enhance the CTR of marketing emails by comparing various tree-based models. Key steps in this research include data collection, feature extraction, and CTR prediction through the evaluation of different models. The statistical results prove that the CatBoost model, with optimized feature selection, achieves near-perfect data fitting, indicating its efficiency.
Authors: Blessington Naveen Palaparthi, S. Akthar
Institution: Acharya Nagarjuna University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408005848