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| author | Sean Owen <srowen@gmail.com> | 2014-08-01 07:32:53 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-08-01 07:32:53 -0700 |
| commit | 82d209d43fb543c174e640667de15b00c7fb5d35 (patch) | |
| tree | 98c3675e5be55718c34aca6bedaff0dc1819e66c /mllib/pom.xml | |
| parent | a32f0fb73a739c56208cafcd9f08618fb6dd8859 (diff) | |
| download | spark-82d209d43fb543c174e640667de15b00c7fb5d35.tar.gz spark-82d209d43fb543c174e640667de15b00c7fb5d35.tar.bz2 spark-82d209d43fb543c174e640667de15b00c7fb5d35.zip | |
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 – 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.
Author: Sean Owen <srowen@gmail.com>
Closes #1687 from srowen/SPARK-2768 and squashes the following commits:
b349675 [Sean Owen] Additional review changes
c9edb04 [Sean Owen] Updates from code review
7bc35f9 [Sean Owen] Add recommend methods to MatrixFactorizationModel
Diffstat (limited to 'mllib/pom.xml')
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