GitHub user daniloascione opened a pull request:
https://github.com/apache/spark/pull/16353
[SPARK-18948][MLlib] Add Mean Percentile Rank metric for ranking algorithms
## What changes were proposed in this pull request?
This PR adds the implementation of Mean Percentile Rank (MPR) metric in
mllib.evaluation, as described in the paper âCollaborative Filtering for
Implicit Feedback Datasets.â (Hu, Y., Y. Koren, and C. Volinsky
doi:10.1109/ICDM.2008.22).
This metric is useful to evaluate recommendations given by the ALS with
implicit feedback.
## How was this patch tested?
Additional test cases have been added to test Mean Percentile Rank (MPR).
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/daniloascione/spark SPARK-18948
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/16353.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 #16353
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commit ed66bb09eddf776e932b29a7e4889128aa775946
Author: Danilo Ascione <[email protected]>
Date: 2016-12-20T16:23:28Z
[SPARK-18948][MLlib] Add Mean Percentile Rank metric for ranking algorithms
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