Object/Class

org.apache.spark.mllib.recommendation

ALS

Related Docs: class ALS | package recommendation

Permalink

object ALS extends Serializable

Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.

Annotations
@Since( "0.8.0" )
Source
ALS.scala
Linear Supertypes
Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. ALS
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  5. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  10. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  11. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  13. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  14. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  15. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  16. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  17. def train(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel

    Permalink

    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 (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    Annotations
    @Since( "0.8.0" )
  18. def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double): MatrixFactorizationModel

    Permalink

    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 (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    Annotations
    @Since( "0.8.0" )
  19. def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel

    Permalink

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

    Permalink

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

    Permalink

    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 (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    Annotations
    @Since( "0.8.1" )
  22. def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double): MatrixFactorizationModel

    Permalink

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

    Permalink

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

    Permalink

    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 (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" )
  25. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped