ABSTRACT
This research project aims to enhance content engagement through hyper-personalized marketing strategies, focusing on a comparative analysis between Random Forest and Recurrent Neural Networks (RNNs). The study involves a detailed exploration of the theoretical foundations, key components, and computational intricacies of Random Forest and RNNs evaluated through data gathered from ClinCalc. To assess their effectiveness in the realm of hyper-personalized marketing, both algorithms are applied to datasets that encompass diverse user preferences and behavioral patterns. The evaluation includes considerations of computational efficiency, adaptability to personalized content, and practical applicability. Our Random Forest model exhibits a robust accuracy of 92.26% in elevating web cookie security, surpassing the performance of Recurrent Neural Networks (RNNs) with their achieved accuracy of 83.36%. A thorough statistical analysis underscores a significant disparity (p = 0.032, independent sample t-test p < 0.05) in cybersecurity performance between the two algorithms, emphasizing the efficacy of our hyper-personalized marketing strategy centered around Random Forest. Random Forest excels in diverse preference adaptation, surpassing Recurrent Neural Networks in hyper-personalized marketing by offering superior adaptability and performance.
