Optimizing Targeted Content Delivery through Reinforcement Learning and Collaborative Filtering Algorithms

Abstract

This research paper explores the optimization of targeted content delivery by integrating reinforcement learning (RL) and collaborative filtering (CF) algorithms to enhance user experience and engagement in digital platforms. Current methods for content recommendation often struggle to balance exploration and exploitation, leading to suboptimal long-term user satisfaction. The proposed hybrid model leverages the strengths of RL in dynamic decision-making and CF in understanding user preferences from historical data. By employing a deep reinforcement learning framework, the system continuously adapts to changes in user behavior, while collaborative filtering mechanisms refine recommendations based on user similarity and item correlation. The integration of these methodologies is assessed through a series of experiments on benchmark datasets, where the hybrid model demonstrates superior performance in metrics such as click-through rate, user retention, and personalization accuracy compared to standalone approaches. This study contributes to the field by providing a robust framework that not only improves recommendation precision but also enhances adaptability and user-centeredness in content delivery systems. Future work will focus on scalability and real-time processing enhancements to further solidify the model's applicability in large-scale environments.