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Spark ML -- Alternating Least Squares (ALS) matrix factorization.

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(), ...)

Arguments

x

An object coercable to a Spark DataFrame (typically, a tbl_spark).
rating.column

The name of the column containing ratings.
user.column

The name of the column containing user IDs.
item.column

The name of the column containing item IDs.
rank

Rank of the factorization.
regularization.parameter

The regularization parameter.
implicit.preferences

Use implicit preference.
alpha

The parameter in the implicit preference formulation.
nonnegative

Use nonnegative constraints for least squares.
iter.max

The maximum number of iterations to use.
ml.options

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.

See also

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