Matrix factorization with explicit ratings, learning is performed by stochastic gradient descent.
The learning algorithm with the supplied parameters
Trained recommender. Signal Predictor sends the output signal only if input Data is present.
Latent features of the users
Latent features of the items
BRISMF widget uses a biased regularized algorithm to factorize a matrix into two low rank matrices as it’s explained in Y. Koren, R. Bell, C. Volinsky, Matrix Factorization Techniques for Recommender Systems. IEE Computer Society, 2009.