object ALS extends Serializable
Toplevel methods for calling Alternating Least Squares (ALS) matrix factorization.
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 @Since( "0.8.0" )
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 ALS.scala
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def
train(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism automatically based on the number of partitions in
ratings
. ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 Annotations
 @Since( "0.8.0" )

def
train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double): MatrixFactorizationModel
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism automatically based on the number of partitions in
ratings
. ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 lambda
regularization parameter
 Annotations
 @Since( "0.8.0" )

def
train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.
 ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 lambda
regularization parameter
 blocks
level of parallelism to split computation into
 Annotations
 @Since( "0.8.0" )

def
train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, seed: Long): MatrixFactorizationModel
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.
 ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 lambda
regularization parameter
 blocks
level of parallelism to split computation into
 seed
random seed for initial matrix factorization model
 Annotations
 @Since( "0.9.1" )

def
trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products.
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism determined automatically based on the number of partitions in
ratings
. ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 Annotations
 @Since( "0.8.1" )

def
trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double): MatrixFactorizationModel
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products.
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism determined automatically based on the number of partitions in
ratings
. ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 lambda
regularization parameter
 alpha
confidence parameter
 Annotations
 @Since( "0.8.1" )

def
trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products.
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.
 ratings
RDD of Rating objects with userID, productID, and rating
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 lambda
regularization parameter
 blocks
level of parallelism to split computation into
 alpha
confidence parameter
 Annotations
 @Since( "0.8.1" )

def
trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double, seed: Long): MatrixFactorizationModel
Train a matrix factorization model given an RDD of 'implicit preferences' given by users to some products, in the form of (userID, productID, preference) pairs.
Train a matrix factorization model given an RDD of 'implicit preferences' given by users to some products, in the form of (userID, productID, preference) pairs. We approximate the ratings matrix as the product of two lowerrank matrices of a given rank (number of features). To solve for these features, we run a given number of iterations of ALS. This is done using a level of parallelism given by
blocks
. ratings
RDD of (userID, productID, rating) pairs
 rank
number of features to use (also referred to as the number of latent factors)
 iterations
number of iterations of ALS
 lambda
regularization parameter
 blocks
level of parallelism to split computation into
 alpha
confidence parameter
 seed
random seed for initial matrix factorization model
 Annotations
 @Since( "0.8.1" )

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