Description: Factorization machines are a generalization of matrix factorization that can capture complex interactions between variables in datasets. These machines are mathematical and computational tools that decompose matrices into products of smaller matrices, facilitating the identification of underlying patterns and relationships. Through this process, they can model nonlinear interactions and dependencies among multiple variables, which is particularly useful in the fields of machine learning and data mining. Factorization machines can handle large volumes of data and are fundamental in model optimization, as they allow for more efficient tuning by identifying the most relevant combinations of features. Their ability to generalize and adapt to different types of data makes them a versatile tool in modern analytics, where precision and efficiency are crucial for the success of predictive models.
