GitHub user srowen opened a pull request:
https://github.com/apache/spark/pull/1687
SPARK-2768 [MLLIB] Add product, user recommend method to
MatrixFactorizationModel
Right now, `MatrixFactorizationModel` can only predict a score for one or
more `(user,product)` tuples. As a comment in the file notes, it would be more
useful to expose a recommend method, that computes top N scoring products for a
user (or vice versa â users for a product).
(This also corrects some long lines in the Java ALS test suite.)
As you can see, it's a little messy to access the class from Java. Should
there be a Java-friendly wrapper for it? with a pointer about where that should
go, I could add that.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/srowen/spark SPARK-2768
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/1687.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 #1687
----
commit 7bc35f9ca6926e968ea9e497f54806eaef4116b8
Author: Sean Owen <[email protected]>
Date: 2014-07-31T10:31:23Z
Add recommend methods to MatrixFactorizationModel
----
---
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.
---
