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  1. Spark
  2. SPARK-18948

Add Mean Percentile Rank metric for ranking algorithms

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    • Type: New Feature
    • Status: Resolved
    • Priority: Major
    • Resolution: Won't Fix
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: MLlib
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      Description

      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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              • Assignee:
                Unassigned
                Reporter:
                danilo.ascione Danilo Ascione
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                  Updated:
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