Enhancing E-commerce Product Recommendations through Hybrid Collaborative Filtering and Deep Reinforcement Learning Algorithms

Abstract

This research paper presents an innovative approach to enhancing e-commerce product recommendations by integrating hybrid collaborative filtering with deep reinforcement learning algorithms. The study addresses the limitations of traditional recommendation systems, which often struggle with issues such as data sparsity, cold-start problems, and the dynamic nature of user preferences. By combining collaborative filtering techniques, which leverage user-item interactions, with advanced deep reinforcement learning models, the proposed system can better capture complex patterns and adapt to changes in user behavior over time. The hybrid model utilizes a matrix factorization-based collaborative filtering method to initially process and predict user preferences. This foundation is then refined through a deep reinforcement learning architecture, specifically a policy gradient method, to continuously learn and optimize recommendations based on real-time user feedback and interactions. Extensive experiments conducted on large-scale e-commerce datasets demonstrate that the integrated approach significantly outperforms traditional recommendation methods in terms of accuracy, precision, and user satisfaction metrics. The paper also explores the computational efficiency of the proposed model, illustrating its scalability and applicability to various e-commerce platforms. The findings suggest that the synergy between collaborative filtering and deep reinforcement learning opens new avenues for creating more personalized and effective recommendation systems, ultimately enhancing the consumer shopping experience and increasing engagement on e-commerce platforms.