Matrix Factorization Techniques in Machine Learning from Dimensionality Reduction to Recommender System

Abstract: Matrix factorization techniques have emerged as powerful tools in machine learning, particularly for their efficacy in dimensionality reduction and recommender systems. This paper explores various matrix factorization methods, including Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and Alternating Least Squares (ALS), highlighting their mathematical foundations and computational frameworks. We discuss the significance of these techniques in reducing the dimensionality of large datasets, enabling efficient data representation and storage while preserving essential information. Furthermore, the application of matrix factorization in recommender systems is examined, illustrating how it facilitates personalized recommendations by uncovering latent user-item interactions. Through comparative analysis and case studies, we demonstrate the effectiveness of these methods in addressing challenges such as sparsity and scalability in recommendation tasks. The paper concludes by identifying future directions for research, emphasizing the integration of matrix factorization with deep learning approaches to enhance model performance and adaptability in dynamic environments.