Matrix Factorization

Matrix factorization is a technique used in data science and artificial intelligence to decompose a large matrix into smaller matrices in order to simplify the data and extract meaningful insights. It is commonly used in recommendation systems, where it is used to predict user preferences based on their past behavior. Matrix factorization works by identifying latent factors that underlie the observed data, and then using these factors to create a lower-dimensional representation of the original matrix. This lower-dimensional representation can then be used to make predictions or to identify patterns in the data that were not apparent in the original matrix. Matrix factorization is a powerful tool for dealing with large, complex datasets, and it has applications in a wide range of fields, including finance, healthcare, and marketing.
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