A Hybrid Recommendation System Using AI Agent, Singular Value Decomposition (SVD), and Non-negative Matrix Factorization (NMF)

Recommendation systems play a crucial role in various applications, including e-commerce, entertainment, and education. This paper presents a hybrid recommendation system combining AI agents with Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) to improve accuracy and efficiency. We evaluate the performance of this approach through a Python implementation, ensuring that the system does not rely on external libraries such as Scikit-Learn. The results are visualized using graphical representations for better interpretability. The proposed model is validated against benchmark datasets, and the experimental results demonstrate its effectiveness in providing accurate recommendations.