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

Abstract

This research paper presents a novel approach to enhancing content personalization at scale by integrating collaborative filtering and reinforcement learning techniques. The study addresses the limitations of traditional methods, such as cold-start problems and scalability issues, by proposing a hybrid model that synergizes the strengths of both collaborative filtering and reinforcement learning. Collaborative filtering, known for its efficiency in leveraging user similarity and item ratings, is enhanced with reinforcement learning, which dynamically adapts content recommendations based on user interaction feedback. A large-scale experimental evaluation was conducted using a dataset from a major online platform, showcasing significant improvements in accuracy and user satisfaction metrics compared to baseline models. The hybrid approach demonstrated superior performance in recommending diverse content, reducing exposure to content silos, and adapting to changing user preferences in real-time. Moreover, the scalability of the proposed system was validated across various content types, ensuring broad applicability in diverse domains. This research provides actionable insights for practitioners aiming to implement efficient, scalable personalization systems, and sets a foundation for future studies on the integration of machine learning methodologies to optimize user experience in digital environments.