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
.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
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
.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
regularization parameter
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.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
regularization parameter
level of parallelism to split computation into
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.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
regularization parameter
level of parallelism to split computation into
random seed for initial matrix factorization model
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
.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
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
.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
regularization parameter
confidence parameter
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.
RDD of Rating objects with userID, productID, and rating
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
regularization parameter
level of parallelism to split computation into
confidence parameter
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
.
RDD of (userID, productID, rating) pairs
number of features to use (also referred to as the number of latent factors)
number of iterations of ALS
regularization parameter
level of parallelism to split computation into
confidence parameter
random seed for initial matrix factorization model
Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.