Matrix Factorization

Matrix factorization is a family of techniques that decompose a data matrix into lower-dimensional components, aiming to reveal underlying structure and reduce dimensionality. Current research emphasizes developing more efficient and robust algorithms, including those incorporating deep learning, handling missing data, and addressing challenges in distributed and federated settings. These advancements are improving the performance and applicability of matrix factorization across diverse fields, such as recommendation systems, computer vision, and natural language processing, by enabling more accurate and scalable data analysis.