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?
