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https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15826309#comment-15826309
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Danilo Ascione commented on SPARK-14409:
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[~srowen] [~mlnick] Also about the top-k problem ("You are comparing the top-k
items as predicted by the model to the top-k items as defined by the label.").
My solution is different in this: it evaluates each label (from the pair
user-item) against the top-k items as predicted by the model (for each user).
Does this makes sense to you?
> Investigate adding a RankingEvaluator to ML
> -------------------------------------------
>
> Key: SPARK-14409
> URL: https://issues.apache.org/jira/browse/SPARK-14409
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Nick Pentreath
> Priority: Minor
>
> {{mllib.evaluation}} contains a {{RankingMetrics}} class, while there is no
> {{RankingEvaluator}} in {{ml.evaluation}}. Such an evaluator can be useful
> for recommendation evaluation (and can be useful in other settings
> potentially).
> Should be thought about in conjunction with adding the "recommendAll" methods
> in SPARK-13857, so that top-k ranking metrics can be used in cross-validators.
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