Matrix factorization is a popular technique in machine learning and data mining that involves decomposing a matrix into a product of two or more matrices. This method is commonly used in collaborative filtering and recommendation systems to predict missing values in a matrix, such as user-item interactions in a ratings matrix. By factorizing the matrix, we can uncover latent features or factors that explain the underlying relationships between rows and columns. Matrix factorization algorithms, such as Singular Value Decomposition (SVD), Alternating Least Squares (ALS), and Non-negative Matrix Factorization (NMF), have been widely studied and applied in various real-world problems to improve prediction accuracy and reduce computational complexity.