As mentioned in Chapter 1, Getting Started with Machine Learning and ML.NET, matrix factorization, by definition, is an unsupervised learning algorithm. This means that the algorithm will train on data and build a matrix of patterns in user ratings, and during a prediction call, will attempt to find like ratings based on the data provided. In this section, we will dive into use cases for matrix factorization and have a look into the matrix factorization trainer in ML.NET.
Use cases for matrix factorizations
Matrix factorizations, as you might be starting to realize, have numerous applications where data is available, but the idea is to suggest other matches based on previously unselected data. Without needing to do manual spot-checking, matrix factorization algorithms train on this unselected data and determine patterns using a key-value pair combination. ML.NET provides various matrix factorization values to look at programmatically, inside of your...
