Danilo Ascione created SPARK-18948:
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Summary: 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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