Reads an ML instance from the input path, a shortcut of read.load(path).

Returns an MLReader instance for this class.

Implementation of the ALS algorithm.

This implementation of the ALS factorization algorithm partitions the two sets of factors among Spark workers so as to reduce network communication by only sending one copy of each factor vector to each Spark worker on each iteration, and only if needed. This is achieved by precomputing some information about the ratings matrix to determine which users require which item factors and vice versa. See the Scaladoc for InBlock for a detailed explanation of how the precomputation is done.

In addition, since each iteration of calculating the factor matrices depends on the known ratings, which are spread across Spark partitions, a naive implementation would incur significant network communication overhead between Spark workers, as the ratings RDD would be repeatedly shuffled during each iteration. This implementation reduces that overhead by performing the shuffling operation up front, precomputing each partition's ratings dependencies and duplicating those values to the appropriate workers before starting iterations to solve for the factor matrices. See the Scaladoc for OutBlock for a detailed explanation of how the precomputation is done.

Note that the term "rating block" is a bit of a misnomer, as the ratings are not partitioned by contiguous blocks from the ratings matrix but by a hash function on the rating's location in the matrix. If it helps you to visualize the partitions, it is easier to think of the term "block" as referring to a subset of an RDD containing the ratings rather than a contiguous submatrix of the ratings matrix.