ml_als_factorization

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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, max.iter = 10L, ...)
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.
max.iter
The maximum number of iterations to use.
...
Optional arguments; currently unused.
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

Aliases
  • ml_als_factorization
Documentation reproduced from package sparklyr, version 0.3.0, License: file LICENSE

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