Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17845#discussion_r114706687
--- Diff: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
---
@@ -356,6 +356,19 @@ class ALSModel private[ml] (
/**
* Makes recommendations for all users (or items).
+ *
+ * Note: the previous approach used for computing top-k recommendations
+ * used a cross-join followed by predicting a score for each row of the
joined dataset.
+ * However, this results in exploding the size of intermediate data.
While Spark SQL makes it
+ * relatively efficient, the approach implemented here is significantly
more efficient.
+ *
+ * This approach groups factors into blocks and computes the top-k
elements per block,
+ * using Level 1 BLAS (dot) and an efficient [[BoundedPriorityQueue]].
It then computes the
--- End diff --
How about "... using dot product instead of gemm and an efficient ..."
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