ALS

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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 lower-rank 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
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 lower-rank 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
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 lower-rank 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
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 lower-rank 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
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 lower-rank 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
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 lower-rank 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
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 lower-rank 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
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 lower-rank 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
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

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@Since( "0.8.1" )
final def wait(): Unit

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Related Docs: class ALS | package recommendation

object ALS extends Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def train(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel

def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double): MatrixFactorizationModel

def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel

def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, seed: Long): MatrixFactorizationModel

def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel

def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double): MatrixFactorizationModel

def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel

def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double, seed: Long): MatrixFactorizationModel

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

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