Enhancing AI-Powered Recommendation Engines Using Collaborative Filtering and Neural Network-Based Algorithms

Abstract

This research paper explores the enhancement of AI-powered recommendation engines by integrating collaborative filtering techniques with advanced neural network-based algorithms. The study addresses the limitations of traditional recommendation systems, which often struggle with scalability, sparse datasets, and dynamic user preferences. By leveraging collaborative filtering, the proposed model effectively captures user-item interactions by analyzing historical data and identifying patterns of shared preferences across users. Simultaneously, neural network architectures, such as deep learning and recurrent neural networks, are utilized to capture complex, non-linear relationships and temporal dynamics in user behavior. The hybrid model is tested across diverse datasets, demonstrating significant improvements in recommendation accuracy, diversity, and user satisfaction compared to conventional systems. Regression analysis and cross-validation techniques are employed to validate the robustness of the model. Additionally, the study examines the computational efficiency and scalability of the proposed approach, providing insights into real-time application feasibility. The findings suggest that the fusion of collaborative filtering with neural network-based algorithms presents a promising direction for future research and practical deployments in personalized recommendation systems across various industries, including e-commerce, entertainment, and social platforms.