Matrix Factorization

Matrix factorization is a mathematical technique that decomposes a matrix into a product of two or more matrices, often used to uncover latent features or reduce dimensionality in data. It is widely applied in machine learning, particularly in recommendation systems, to predict missing values or identify underlying patterns. Common algorithms include Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and Alternating Least Squares (ALS).

Also known as: MF, Matrix Decomposition, Latent Factor Models, SVD-based methods, NMF

Why learn Matrix Factorization?

Developers should learn matrix factorization when building recommendation systems, such as for e-commerce or streaming services, to predict user preferences based on sparse data like ratings. It is also useful in natural language processing for topic modeling and in computer vision for image compression or feature extraction, as it efficiently handles large, high-dimensional datasets by reducing noise and computational complexity.

Learning Resources

• docs: Matrix Factorization for Recommender Systems
• course: Matrix Factorization in Machine Learning - Coursera Course
• tutorial: Matrix Factorization Tutorial by Stanford
• video: Introduction to Matrix Factorization - YouTube Video
• book: Matrix Factorization for Data Science - Book Chapter