Enhancing Content Personalization at Scale Using Deep Reinforcement Learning and Collaborative Filtering Techniques

Abstract

This research paper explores the synergistic integration of deep reinforcement learning (DRL) and collaborative filtering (CF) techniques to enhance content personalization at scale. With the exponential growth of digital content, the challenge of delivering personalized experiences that cater to individual user preferences has become paramount. Traditional personalization methods, while effective to a certain extent, often struggle with scalability and adaptability in dynamic environments. To address these challenges, we propose a novel framework that leverages DRL's capacity for learning complex user interaction patterns and CF's proficiency in harnessing user-item relationships. The DRL model is trained to make sequential decisions, adjusting content recommendations in real-time based on user interactions, while the CF component enhances prediction accuracy by analyzing user similarity matrices and latent factors. Our approach is tested on large-scale datasets, demonstrating significant improvements in user satisfaction metrics, engagement rates, and system efficiency compared to baseline models. We further analyze the model's ability to adapt to evolving user preferences and its robustness in handling sparse and noisy data. The findings underscore the potential of combining DRL and CF to push the boundaries of content personalization, offering a scalable solution for content providers aiming to deliver more relevant and engaging user experiences. This study not only contributes to the theoretical understanding of personalized content delivery systems but also provides practical insights for deploying such systems in real-world applications.