ml_als_factorization {sparklyr} | R Documentation |
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(), ...)
x |
An object coercable to a Spark DataFrame (typically, a
|
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
|
... |
Optional arguments. The |
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