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From "Joseph K. Bradley (JIRA)" <>
Subject [jira] [Commented] (SPARK-18948) Add Mean Percentile Rank metric for ranking algorithms
Date Sat, 07 Jan 2017 19:37:58 GMT


Joseph K. Bradley commented on SPARK-18948:

Oh I agree we'd need to add the initial RankingEvaluator first.  It looks like there is a
PR, though I haven't had a chance to look at it yet.

> Add Mean Percentile Rank metric for ranking algorithms
> ------------------------------------------------------
>                 Key: SPARK-18948
>                 URL:
>             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.
( (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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