Leveraging Reinforcement Learning and Collaborative Filtering for Enhanced AI-Driven Targeted Content Delivery

Abstract

This research paper explores the integration of reinforcement learning (RL) with collaborative filtering (CF) techniques to optimize AI-driven targeted content delivery systems. As digital platforms strive to provide personalized experiences to their users, the challenge of accurately predicting content preferences becomes increasingly complex. Traditional collaborative filtering methods, albeit effective in many scenarios, often struggle with issues such as data sparsity and cold-start problems. Reinforcement learning, with its ability to learn optimal policies through interaction with dynamic environments, presents a promising avenue for addressing these limitations. In this study, we propose a novel framework that synergizes RL with CF, leveraging the strengths of both methodologies to enhance content recommendation mechanisms. We design a hybrid model where CF algorithms are utilized to initialize the recommendation process, while an RL agent refines and adapts the recommendations based on real-time user interactions. This approach not only improves the accuracy of content recommendations but also ensures adaptability to evolving user preferences. Extensive experiments conducted on benchmark datasets demonstrate significant improvements in recommendation precision and user engagement compared to traditional methods. Furthermore, the proposed model exhibits robust performance in scenarios with sparse data and new user introductions. These findings underscore the potential of merging reinforcement learning with collaborative filtering to advance the state-of-the-art in AI-driven content delivery systems and pave the way for more intelligent and responsive digital experiences.