Github user atalwalkar commented on a diff in the pull request:
https://github.com/apache/spark/pull/2064#discussion_r16500714
--- Diff: 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 introduced in the paper
--- End diff --
This technique was used by many groups in the Netflix Prize (see section 5
of this paper for some more references:
http://www.cs.toronto.edu/~rsalakhu/papers/weighted_tc.pdf). Perhaps this
sentence should be changed to "This approach is discussed in the following
paper..."
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
