Movie ratings prediction model trained by FM using pyFM library.

Factorization Machines (FM) uses stochastic gradient descent with adaptive regularization as a learning method, which adapts the regularization automatically while training the model parameters. It combines the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. FM is especially appealing for prediction problems over the data sets with large categorical variables because it allows to estimate variable interactions reliably due to factorized interactions.

[1] Steffen Rendle (2012): Factorization Machines with libFM, in ACM Trans. Intell. Syst. Technol., 3(3), May.