[SPARK-2843][MLLIB] add a section about regularization parameter in ALS

atalwalkar srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #2064 from mengxr/als-doc and squashes the following commits:

b2e20ab [Xiangrui Meng] introduced -> discussed
98abdd7 [Xiangrui Meng] add reference
339bd08 [Xiangrui Meng] add a section about regularization parameter in ALS

1 files changed, 11 insertions, 0 deletions

diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index ab10b2f01f..d5c539db79 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -43,6 +43,17 @@ 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.
+This approach is named "ALS-WR" and discussed in the paper
+"[Large-Scale Parallel Collaborative Filtering for the Netflix Prize](http://dx.doi.org/10.1007/978-3-540-68880-8_32)".
+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">