Matrix factorization: Revision history

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20 March 2023

  • curprev 11:4211:42, 20 March 2023‎ Walle talk contribs‎ 4,027 bytes +4,027‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Matrix factorization is a technique in machine learning that aims to discover latent features underlying the interactions between two different kinds of entities. It has been widely used for tasks such as recommendation systems, dimensionality reduction, and data imputation. The primary goal of matrix factorization is to approximate a given matrix by factorizing it into two or more lower-dimensional matrices, which can..."
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