ml_als_factorization
From sparklyr v0.3.5
by Javier Luraschi
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
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