Perform alternating least squares matrix factorization on a Spark DataFrame.
ml_als_factorization(x, rating.column = "rating", user.column = "user",
item.column = "item", rank = 10L, regularization.parameter = 0.1,
implicit.preferences = FALSE, alpha = 1, nonnegative = FALSE,
iter.max = 10L, ml.options = ml_options(), ...)An object coercable to a Spark DataFrame (typically, a
tbl_spark).
The name of the column containing ratings.
The name of the column containing user IDs.
The name of the column containing item IDs.
Rank of the factorization.
The regularization parameter.
Use implicit preference.
The parameter in the implicit preference formulation.
Use nonnegative constraints for least squares.
The maximum number of iterations to use.
Optional arguments, used to affect the model generated. See
ml_options for more details.
Optional arguments. The data argument can be used to
specify the data to be used when x is a formula; this allows calls
of the form ml_linear_regression(y ~ x, data = tbl), and is
especially useful in conjunction with do.
Other Spark ML routines: ml_decision_tree,
ml_generalized_linear_regression,
ml_gradient_boosted_trees,
ml_kmeans, ml_lda,
ml_linear_regression,
ml_logistic_regression,
ml_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest, ml_pca,
ml_random_forest,
ml_survival_regression