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

Abstract

This research paper explores the development of real-time product recommendation engines utilizing a hybrid methodology that combines collaborative filtering with deep reinforcement learning (DRL) algorithms to enhance user personalization and engagement. Traditional collaborative filtering techniques, while effective in leveraging user-item interactions, often face challenges such as data sparsity and cold-start problems. To address these issues, we integrate DRL frameworks capable of learning optimal recommendation strategies through dynamic interactions with users. By employing a Markov Decision Process (MDP) model, the system predicts user preferences based on historical data and real-time feedback, allowing for continuous improvement of recommendation accuracy. The study implements a dual-layer architecture: the first layer utilizes user-based and item-based collaborative filtering to generate an initial candidate list of products, and the second layer employs a DRL agent to re-rank these candidates by considering long-term user satisfaction rewards. Experimental evaluation on benchmark datasets demonstrates that the hybrid approach significantly outperforms traditional methods in terms of precision, recall, and user engagement rates. Our findings suggest that integrating DRL with collaborative filtering not only enhances recommendation effectiveness but also provides a scalable solution for businesses adapting to evolving consumer behavior in dynamic online environments.