ALS

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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6. def analyzeBlocks(ratings: RDD[Rating], numUserBlocks: Int, numProductBlocks: Int): Array[BlockStats]

   :: DeveloperApi :: Given an RDD of ratings, number of user blocks, and number of product blocks, computes the statistics of each block in ALS computation.

   :: DeveloperApi :: Given an RDD of ratings, number of user blocks, and number of product blocks, computes the statistics of each block in ALS computation. This is useful for estimating cost and diagnosing load balance.

   ratings

   an RDD of ratings

   numUserBlocks

   number of user blocks

   numProductBlocks

   number of product blocks

   returns

   statistics of user blocks and product blocks

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   @DeveloperApi()

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

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20. 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)

21. 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)

22. 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

23. 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

24. 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)

25. 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

26. 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

27. 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

28. final def wait(): Unit

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

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

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