Matrix Factorization

Matrix factorization is a mathematical technique used to break down a large, complex data table into smaller, simpler parts called factors. Imagine a big grid showing user preferences for movies; matrix factorization finds hidden patterns by representing users and movies as sets of smaller, related matrices. This helps predict missing data, like which movies a user might like. It's widely used in recommendation systems, such as suggesting products, music, or movies, by uncovering underlying relationships in data that aren’t immediately obvious.