Details
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New Feature
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Status: Resolved
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Major
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Resolution: Won't Fix
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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).
Attachments
Issue Links
- relates to
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SPARK-3568 Add metrics for ranking algorithms
- Resolved
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SPARK-14409 Investigate adding a RankingEvaluator to ML
- Resolved
- links to