Leveraging Reinforcement Learning and Collaborative Filtering for Enhanced Personalization in Loyalty Programs

Abstract

This research paper explores the integration of reinforcement learning (RL) and collaborative filtering (CF) techniques to enhance personalization in loyalty programs, aiming to improve user engagement and satisfaction. The study proposes a novel hybrid framework that leverages RL's dynamic decision-making capabilities and CF's ability to identify patterns in user preferences, creating a robust system for personalized reward recommendations. The research begins by analyzing the limitations of current loyalty programs, which often rely on static models and generalized rewards that fail to address individual user behavior effectively. A detailed methodology is presented, wherein an RL agent is trained to dynamically adapt to user interactions and preferences over time, while CF is employed to extract latent features from historical data, providing a comprehensive understanding of user needs. The paper further demonstrates the implementation of this framework in a simulated loyalty program environment, showcasing its ability to increase user retention and program engagement compared to traditional methods. Results indicate a significant improvement in personalization accuracy, with a noted increase in user satisfaction metrics. This study concludes by discussing the implications of combining RL and CF for loyalty program providers, suggesting that the proposed framework not only enhances user experience but also provides strategic insights for tailoring future business offerings. The research contributes to the emerging field of AI-driven personalization, offering a scalable solution applicable to a wide range of customer loyalty systems.