Hi, I have a question about the block size to be specified in ALS.trainImplicit() in pyspark (Spark 1.6.1). There is only one block size parameter to be specified. I want to know if that would result in partitioning both the users as well as the items axes.
For example, I am using the following call to ALs.trainImplicit() in my code. --------------- RANK = 50 ITERATIONS = 2 BLOCKS = 1000 ALPHA = 1.0 model = ALS.trainImplicit(ratings, RANK, ITERATIONS, blocks=BLOCKS, alpha=ALPHA) ---------------- Will this partition the users x items matrix into BLOCKS x BLOCKS number of matrices or will it partition only the users axis thereby resulting in BLOCKS number of matrices, each with columns = total number of unique items? Thanks, Nik
