Leveraging Reinforcement Learning and Collaborative Filtering for Enhanced AI-Driven Product Discovery Tools

Abstract

This research paper explores the integration of reinforcement learning (RL) and collaborative filtering (CF) to enhance the effectiveness of AI-driven product discovery tools, which are pivotal for personalized recommendations in digital marketplaces. The proposed hybrid framework leverages the strengths of RL in real-time decision-making and CF's ability to utilize historical user behavior data. The RL component dynamically adapts to user interactions, optimizing recommendation strategies to maximize engagement and satisfaction. Simultaneously, CF enriches the RL model by providing insights into patterns of user preferences based on the similarity of past behaviors. We conducted extensive experiments on large-scale e-commerce datasets to evaluate the performance of the integrated approach against standalone RL and CF models. The results demonstrate a significant improvement in recommendation accuracy, user retention, and click-through rates. Furthermore, the system's adaptability to shifting user preferences is highlighted, showcasing the potential for maintaining high recommendation quality over time. This study offers valuable insights into the synergistic potential of combining RL and CF, underscoring its implications for the future development of AI-driven recommendation systems that are both robust and customer-centric.