diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index ab10b2f01f87b..9581bb21918ca 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/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