Matrix Factorization in Collaborative Filtering

Matrix factorization is a technique used in collaborative filtering to recommend items, such as movies or products, based on user preferences. It involves breaking down a large user-item interaction matrix (where rows represent users and columns represent items) into smaller, simpler matrices that capture underlying patterns. By identifying relationships between users and items, this method can predict how much a user might like an item they haven't seen yet. Essentially, it helps highlight hidden connections in the data, allowing services to suggest relevant items tailored to individual tastes.