addressed comments

mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala

@@ -278,6 +278,9 @@ class ALS extends Estimator[ALSModel] with ALSParams {
   /** @group setParam */
   def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
 
+  /** @group setParam */
+  def setSeed(value: Long): this.type = set(seed, value)
+
   /**
    * Sets both numUserBlocks and numItemBlocks to the specific value.
    * @group setParam

python/pyspark/ml/recommendation.py

@@ -29,30 +29,39 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     """
     Alternating Least Squares (ALS) matrix factorization.
 
-    ALS attempts to estimate the ratings matrix `R` as the product of two lower-rank matrices,
-    `X` and `Y`, i.e. `X * Yt = R`. Typically these approximations are called 'factor' matrices.
-    The general approach is iterative. During each iteration, one of the factor matrices is held
-    constant, while the other is solved for using least squares. The newly-solved factor matrix is
-    then held constant while solving for the other factor matrix.
-
-    This is a blocked implementation of the ALS factorization algorithm that groups the two sets
-    of factors (referred to as "users" and "products") into blocks and reduces communication by only
-    sending one copy of each user vector to each product block on each iteration, and only for the
-    product blocks that need that user's feature vector. This is achieved by pre-computing some
-    information about the ratings matrix to determine the "out-links" of each user (which blocks of
-    products it will contribute to) and "in-link" information for each product (which of the feature
-    vectors it receives from each user block it will depend on). This allows us to send only an
-    array of feature vectors between each user block and product block, and have the product block
-    find the users' ratings and update the products based on these messages.
+    ALS attempts to estimate the ratings matrix `R` as the product of
+    two lower-rank matrices, `X` and `Y`, i.e. `X * Yt = R`. Typically
+    these approximations are called 'factor' matrices. The general
+    approach is iterative. During each iteration, one of the factor
+    matrices is held constant, while the other is solved for using least
+    squares. The newly-solved factor matrix is then held constant while
+    solving for the other factor matrix.
+
+    This is a blocked implementation of the ALS factorization algorithm
+    that groups the two sets of factors (referred to as "users" and
+    "products") into blocks and reduces communication by only sending
+    one copy of each user vector to each product block on each
+    iteration, and only for the product blocks that need that user's
+    feature vector. This is achieved by pre-computing some information
+    about the ratings matrix to determine the "out-links" of each user
+    (which blocks of products it will contribute to) and "in-link"
+    information for each product (which of the feature vectors it
+    receives from each user block it will depend on). This allows us to
+    send only an array of feature vectors between each user block and
+    product block, and have the product block find the users' ratings
+    and update the products based on these messages.
 
     For implicit preference data, the algorithm used is based on
-    "Collaborative Filtering for Implicit Feedback Datasets", available at
-    `http://dx.doi.org/10.1109/ICDM.2008.22`, adapted for the blocked approach used here.
-
-    Essentially instead of finding the low-rank approximations to the rating matrix `R`,
-    this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if
-    r > 0 and 0 if r <= 0. The ratings then act as 'confidence' values related to strength of
-    indicated user preferences rather than explicit ratings given to items.
+    "Collaborative Filtering for Implicit Feedback Datasets", available
+    at `http://dx.doi.org/10.1109/ICDM.2008.22`, adapted for the blocked
+    approach used here.
+
+    Essentially instead of finding the low-rank approximations to the
+    rating matrix `R`, this finds the approximations for a preference
+    matrix `P` where the elements of `P` are 1 if r > 0 and 0 if r <= 0.
+    The ratings then act as 'confidence' values related to strength of
+    indicated user preferences rather than explicit ratings given to
+    items.
 
     >>> als = ALS(rank=10, maxIter=5)
     >>> model = als.fit(df)