Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11186#discussion_r52976896
--- Diff: python/pyspark/mllib/recommendation.py ---
@@ -249,11 +277,39 @@ def train(cls, ratings, rank, iterations=5,
lambda_=0.01, blocks=-1, nonnegative
def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01,
blocks=-1, alpha=0.01,
nonnegative=False, seed=None):
"""
- Train a matrix factorization model given an RDD of 'implicit
preferences' given by users
- to some products, in the form of (userID, productID, preference)
pairs. We approximate the
- ratings matrix as the product of two lower-rank matrices of a
given rank (number of
- features). To solve for these features, we run a given number of
iterations of ALS.
- This is done using a level of parallelism given by `blocks`.
+ Train a matrix factorization model given an RDD of 'implicit
+ preferences' given by users to some products, in the form of
+ (userID, productID, preference) pairs. We approximate the ratings
--- End diff --
Same comment as above applies
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