Machine Learning Engineering
Matrix factorization is a mathematical technique used to decompose a matrix into a product of two or more matrices, making it easier to analyze and understand complex data structures. This approach is particularly useful in recommender systems, where it helps uncover latent factors that explain observed interactions between users and items, facilitating personalized recommendations. By identifying these underlying patterns, matrix factorization enhances the ability of systems to predict user preferences based on historical data.
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