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https://issues.apache.org/jira/browse/SPARK-18948?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15796730#comment-15796730
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Joseph K. Bradley commented on SPARK-18948:
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OK, but please say if you'd like to continue this work under the
DataFrame-based API. Thanks again.
> 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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