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Posted to issues@spark.apache.org by "Danilo Ascione (JIRA)" <ji...@apache.org> on 2017/01/02 14:18:58 UTC
[jira] [Commented] (SPARK-18948) Add Mean Percentile Rank metric
for ranking algorithms
[ https://issues.apache.org/jira/browse/SPARK-18948?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15792934#comment-15792934 ]
Danilo Ascione commented on SPARK-18948:
----------------------------------------
I understand. I'll close the PR.
Thank you
> Add Mean Percentile Rank metric for ranking algorithms
> ------------------------------------------------------
>
> Key: SPARK-18948
> URL: https://issues.apache.org/jira/browse/SPARK-18948
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Danilo Ascione
>
> Add the Mean Percentile Rank (MPR) metric for ranking algorithms, as described in the paper :
> Hu, Y., Y. Koren, and C. Volinsky. “Collaborative Filtering for Implicit Feedback Datasets.” In 2008 Eighth IEEE International Conference on Data Mining, 263–72, 2008. doi:10.1109/ICDM.2008.22. (http://yifanhu.net/PUB/cf.pdf) (NB: MPR is called "Expected percentile rank" in the paper)
> The ALS algorithm for implicit feedback in Spark ML is based on the same paper.
> Spark ML lacks an implementation of an appropriate metric for implicit feedback, so the MPR metric can fulfill this use case.
> This implementation add the metric to the RankingMetrics class under org.apache.spark.mllib.evaluation (SPARK-3568), and it uses the same input (prediction and label pairs).
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