ALS

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17. 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 (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

    

    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

    

    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

    

    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

    

    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

    

    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

    

    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

    

    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

    

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

    

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27. final def wait(arg0: Long): Unit

    

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