Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11186#discussion_r53283890
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala ---
@@ -329,13 +327,12 @@ object ALS {
}
/**
- * Train a matrix factorization model given an RDD of ratings given by
users to some products,
- * in the form of (userID, productID, rating) 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. The level of
parallelism is determined
- * automatically based on the number of partitions in `ratings`.
+ * Train a matrix factorization model given an RDD of ratings by users
for a subset of products.
+ * The ratings matrix is approximated as the product of two lower-rank
matrices of a given rank
+ * (number of features). To solve for these features, ALS is run
iteratively with a level of
+ * parallelism automatically based on the number of partitions in
`ratings`.
*
- * @param ratings RDD of (userID, productID, rating) pairs
+ * @param ratings RDD of [[Rating]] objects with userID, productID,
and rating
* @param rank number of features to use
* @param iterations number of iterations of ALS (recommended: 10-20)
* @param lambda regularization factor (recommended: 0.01)
--- End diff --
While we're at it, I don't like the `(recommended: 0.01)` in the comment
for `lambda`. This is the default for the method call without specifying
`lambda`, but I certainly don't think this is the **recommended**
regularization. Lambda can vary widely depending on the dataset
characteristics, and should be selected in the usual manner (e.g. through
cross-validation).
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