Factorization Machines

Factorization Machines (FM) is a supervised machine learning algorithm that can be used for both regression and classification tasks. It is particularly useful when dealing with sparse datasets, where there are many missing values. FM is based on matrix factorization, which is a technique used to decompose a matrix into two or more matrices that can be multiplied together to reconstruct the original matrix. In FM, the input data is represented as a feature matrix, and the algorithm learns a low-dimensional representation of the data by factorizing the feature matrix into two matrices: one that represents the feature interactions and another that represents the feature weights. This allows FM to capture complex feature interactions and make accurate predictions even when there are many missing values in the data.
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