ALS

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

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) pairs.

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) 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. The level of parallelism is determined automatically based on the number of partitions in ratings.

    ratings

    RDD of (userID, productID, rating) pairs

    rank

    number of features to use

    iterations

    number of iterations of ALS (recommended: 10-20)

    Annotations

    @Since( "0.8.0" )

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

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) pairs.

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) 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. The level of parallelism is determined automatically based on the number of partitions in ratings.

    ratings

    RDD of (userID, productID, rating) pairs

    rank

    number of features to use

    iterations

    number of iterations of ALS (recommended: 10-20)

    lambda

    regularization factor (recommended: 0.01)

    Annotations

    @Since( "0.8.0" )

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

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) pairs.

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) 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 (recommended: 10-20)

    lambda

    regularization factor (recommended: 0.01)

    blocks

    level of parallelism to split computation into

    Annotations

    @Since( "0.8.0" )

22. 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 given by users to some products, in the form of (userID, productID, rating) pairs.

    Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) 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 (recommended: 10-20)

    lambda

    regularization factor (recommended: 0.01)

    blocks

    level of parallelism to split computation into

    seed

    random seed

    Annotations

    @Since( "0.9.1" )

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

    Train a matrix factorization model given an RDD of 'implicit preferences' ratings given by users to some products, in the form of (userID, productID, rating) pairs.

    Train a matrix factorization model given an RDD of 'implicit preferences' ratings given by users to some products, in the form of (userID, productID, rating) 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. The level of parallelism is determined automatically based on the number of partitions in ratings. Model parameters alpha and lambda are set to reasonable default values

    ratings

    RDD of (userID, productID, rating) pairs

    rank

    number of features to use

    iterations

    number of iterations of ALS (recommended: 10-20)

    Annotations

    @Since( "0.8.1" )

24. 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' 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. The level of parallelism is determined automatically based on the number of partitions in ratings.

    ratings

    RDD of (userID, productID, rating) pairs

    rank

    number of features to use

    iterations

    number of iterations of ALS (recommended: 10-20)

    lambda

    regularization factor (recommended: 0.01)

    alpha

    confidence parameter

    Annotations

    @Since( "0.8.1" )

25. 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' 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 (recommended: 10-20)

    lambda

    regularization factor (recommended: 0.01)

    blocks

    level of parallelism to split computation into

    alpha

    confidence parameter

    Annotations

    @Since( "0.8.1" )

26. 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 (recommended: 10-20)

    lambda

    regularization factor (recommended: 0.01)

    blocks

    level of parallelism to split computation into

    alpha

    confidence parameter

    seed

    random seed

    Annotations

    @Since( "0.8.1" )

27. final def wait(): Unit

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

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

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