Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

Algorithm 1

GPU-accelerated NMF on Spark.

	Input: Original matrix , low rank and iteration times
	Input: Context of Spark Environment
	Input: Number of executors and number of data partitions
	Input: Data collection of matrix elements and for matrices and
	Input: Data collection in the form of RDD and for matrices and
	Output: Matrices and after decomposition
	(1) generate initial , by random
	(2)
	(3) broadcast
	(4) for = 1: do
	(5)
	(6) //update
	(7) call
	(8) function mapH(data, result)
	(9)
	(10)
	(11)
	(12)
	(13)
	(14)
	(15) end function
	(16)
	(17)
	(18) //update
	(19) call
	(20) function mapW(data, result)
	(21)
	(22)
	(23)
	(24)
	(25)
	(26)
	(27) end function
	(28)
	(29) end for
	(30)