add a section about regularization parameter in ALS

docs/mllib-collaborative-filtering.md

@@ -43,6 +43,15 @@ level of confidence in observed user preferences, rather than explicit ratings g
 model then tries to find latent factors that can be used to predict the expected preference of a
 user for an item.
 
+### Scaling of the regularization parameter
+
+Since v1.1, we scale the regularization parameter `lambda` in solving each least squares problem by
+the number of ratings the user generated in updating user factors,
+or the number of ratings the product received in updating product factors.
+It makes `lambda` 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.
+
 ## Examples
 
 <div class="codetabs">