ml_als_factorization
Spark ML -- Alternating Least Squares (ALS) matrix factorization.
Perform alternating least squares matrix factorization on a Spark DataFrame.
Usage
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_optionsfor more details.- ...
Optional arguments. The
dataargument can be used to specify the data to be used whenxis a formula; this allows calls of the formml_linear_regression(y ~ x, data = tbl), and is especially useful in conjunction withdo.
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