Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17102#discussion_r103757179
--- Diff: docs/ml-collaborative-filtering.md ---
@@ -59,6 +59,34 @@ This approach is named "ALS-WR" and discussed in the
paper
It makes `regParam` less dependent on the scale of the dataset, so we can
apply the
best parameter learned from a sampled subset to the full dataset and
expect similar performance.
+### Cold-start strategy
+
+When making predictions using an `ALSModel`, it is common to encounter
users and/or items in the
+test dataset that were not present during training the model. This
typically occurs in two
+scenarios:
+
+1. In production, for new users or items that have no rating history and
on which the model has not
+been trained (this is the "cold start problem")
--- End diff --
sure thing
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