matrix factorization
[/ˈmeɪtrɪks fæktərɪˈzeɪʃən/]
nounpl: matrix factorizations
fatoração de matrizes
1. A mathematical technique that decomposes a matrix into a product of two or more matrices, commonly used in machine learning, linear algebra, and data analysis.
Matrix factorization is widely used in recommendation systems to predict user preferences.
A fatoração de matrizes é amplamente utilizada em sistemas de recomendação para prever preferências de usuários.
2. In collaborative filtering, a method that breaks down a user-item interaction matrix to identify latent factors representing user preferences and item characteristics.
The recommendation engine employed matrix factorization to uncover hidden patterns in customer behavior.
O mecanismo de recomendação utilizou fatoração de matrizes para descobrir padrões ocultos no comportamento dos clientes.
3. A decomposition process that expresses a matrix as a product of lower-rank matrices, reducing dimensionality and computational complexity.
Through matrix factorization, we reduced the dimensionality of the dataset while preserving essential information.
Através da fatoração de matrizes, reduzimos a dimensionalidade do conjunto de dados mantendo informações essenciais.
Matrix factorization is a fundamental concept in machine learning and data science that has become increasingly important with the growth of artificial intelligence and big data analytics. The term is used consistently across English and Portuguese-speaking tech communities, particularly in the context of Netflix-style recommendation systems and modern AI applications. In Brazil, this terminology is standard in computer science departments, tech startups, and data science teams.
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