With Spark 1.5, the following code: from pyspark import SparkContext, SparkConf from pyspark.mllib.recommendation import ALS, Rating r1 = (1, 1, 1.0) r2 = (1, 2, 2.0) r3 = (2, 1, 2.0) ratings = sc.parallelize([r1, r2, r3]) model = ALS.trainImplicit(ratings, 1, seed=10)
res = model.recommendProductsForUsers(2) raises the error --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-8-c65e6875ea5b> in <module>() 7 model = ALS.trainImplicit(ratings, 1, seed=10) 8 ----> 9 res = model.recommendProductsForUsers(2) AttributeError: 'MatrixFactorizationModel' object has no attribute 'recommendProductsForUsers' If the method is not available, is there a workaround with a large number of users and products?