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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-18948) Add Mean Percentile Rank metric for ranking algorithms
Date Tue, 20 Dec 2016 16:35:58 GMT

    [ https://issues.apache.org/jira/browse/SPARK-18948?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15764601#comment-15764601
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Apache Spark commented on SPARK-18948:
--------------------------------------

User 'daniloascione' has created a pull request for this issue:
https://github.com/apache/spark/pull/16353

> 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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