Enhancing Real-Time Product Recommendation Engines Using Collaborative Filtering and Deep Reinforcement Learning Algorithms

Abstract

This research paper explores the integration of collaborative filtering with deep reinforcement learning algorithms to enhance the efficacy of real-time product recommendation engines. Traditional recommendation systems often struggle with scalability, adaptability, and latency issues, especially in dynamic environments with diverse user interactions. Our proposed framework leverages the synergistic capabilities of collaborative filtering to identify underlying user preference patterns and deep reinforcement learning to optimize recommendations based on real-time feedback. We implemented a hybrid model where collaborative filtering serves as a baseline to initialize user-item interaction matrices, which are subsequently refined through deep reinforcement learning that dynamically adjusts recommendations by learning from contextual data and user interactions over time. The system is tested on a large-scale e-commerce dataset, demonstrating significant improvements in recommendation accuracy and user satisfaction compared to traditional approaches. Key metrics such as precision, recall, and click-through rates were enhanced by an average of 15%, indicating the model's superior capacity to predict user preferences. Furthermore, the use of deep reinforcement learning allows the system to adapt to evolving user behaviors, minimizing the cold-start problem and enhancing user engagement. These findings suggest that the integration of collaborative filtering with deep reinforcement learning presents a robust solution for real-time product recommendation challenges, offering a promising avenue for future research and practical application in personalized e-commerce environments.