GitHub user coderxiang opened a pull request:
https://github.com/apache/spark/pull/1026
SPARK-2085: [MLlikb] Apply user-specific regularization instead of uniform
regularization in ALS
The current implementation of ALS takes a single regularization parameter
and apply it on both of the user factors and the product factors. This kind of
regularization can be less effective while users number is significantly larger
than the number of products (and vice versa). For example, if we have 10M users
and 1K product, regularization on user factors will dominate. Following the
discussion in [this
thread](http://apache-spark-user-list.1001560.n3.nabble.com/possible-bug-in-Spark-s-ALS-implementation-tt2567.html#a2704),
the implementation in this PR will regularize each factor vector by #ratings *
lambda.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/coderxiang/spark als-reg
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/1026.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1026
----
commit c28d1f9a7ec8b812300b83187199ef7043a5b15c
Author: Shuo Xiang <sxi...@twitter.com>
Date: 2014-06-09T22:13:25Z
Apply user-specific regularization instead of uniform regularization in
Alternating Least Squares (ALS)
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