Enhancing Digital Advertising through AI-Powered Personalization: Leveraging Reinforcement Learning and Collaborative Filtering Algorithms

Abstract

This research paper explores the advancement of digital advertising by integrating AI-powered personalization techniques through reinforcement learning and collaborative filtering algorithms. The study aims to address the growing need for more effective and user-centric advertising strategies in the digital age, capitalizing on the vast amounts of data generated by user interactions online. By employing reinforcement learning, the system continuously improves its personalization strategies by receiving real-time feedback from user interactions, enhancing the adaptability and efficiency of ad placements. Simultaneously, collaborative filtering is utilized to analyze patterns and correlations within user data, enabling more precise targeting based on shared preferences and behaviors. The research involves the development of a hybrid model that amalgamates these AI techniques to create a robust personalization framework. Comprehensive experiments conducted on various datasets demonstrate significant improvements in user engagement and conversion rates compared to traditional advertising methods. The results indicate that this AI-driven approach not only enhances user experience by delivering relevant content but also optimizes advertising revenue for businesses by reducing wastage and increasing the likelihood of successful conversions. This paper contributes to the field of digital marketing by providing a scalable and dynamic personalization solution that can be adapted to evolving market demands and user preferences.