Enhancing User Experience with AI-Powered Recommendation Engines: A Comparative Study of Collaborative Filtering, Neural Collaborative Filtering, and Matrix Factorization Algorithms

Abstract

This research paper delves into the efficacy of AI-powered recommendation engines in enhancing user experience, focusing on a comparative analysis of three prominent algorithms: Collaborative Filtering (CF), Neural Collaborative Filtering (NCF), and Matrix Factorization (MF). The study is motivated by the growing reliance on personalized recommendations in digital platforms to augment user satisfaction and engagement. We systematically evaluate the performance of these algorithms across multiple datasets, varying in size and domain, to assess their accuracy, scalability, and computational efficiency. Key metrics such as precision, recall, and F1-score are employed to measure recommendation quality, while processing time and memory usage are analyzed for efficiency insights. Our findings indicate that while traditional CF offers simplicity and interpretability, NCF demonstrates superior accuracy in capturing complex user-item interactions through deep learning frameworks. Conversely, MF strikes a balance between computational efficiency and recommendation quality, benefiting from its probabilistic approach to latent factor modeling. Through this comparative study, we provide actionable insights into selecting and deploying optimal recommendation systems tailored to specific user and business needs. The paper concludes with a discussion on potential enhancements and the integration of hybrid models to further refine recommendation accuracy and user satisfaction in future deployments.