Enhancing Personalized E-commerce Experiences through Deep Reinforcement Learning and Collaborative Filtering Algorithms

Abstract

This research paper explores the integration of deep reinforcement learning (DRL) and collaborative filtering algorithms to enhance personalized e-commerce experiences. The study addresses the limitations of traditional recommendation systems, which often fail to adapt to users' dynamic preferences and behaviors in real-time. We propose a novel hybrid model that employs DRL to continuously learn and adapt decision-making strategies, optimizing the recommendation process based on evolving user interactions. Simultaneously, collaborative filtering is utilized to leverage user-item interaction history, enhancing the model's predictive accuracy by capturing latent community preferences. The hybrid approach is empirically validated using large-scale e-commerce datasets, demonstrating significant improvements in recommendation relevance, user satisfaction, and engagement metrics compared to baseline models. Furthermore, the integration of DRL facilitates the exploration of novel user preferences, offering a robust mechanism for personalization in fluctuating market environments. The paper concludes with a discussion on the theoretical implications of merging DRL with collaborative filtering, potential challenges, and future research directions aimed at refining e-commerce personalizations through advanced machine learning techniques.