[ https://issues.apache.org/jira/browse/SPARK-4231?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14200470#comment-14200470 ]
Sean Owen commented on SPARK-4231: ---------------------------------- Yes I'm mostly questioning implementing this in examples. The definition in RankingMetrics looks like the usual one to me -- average from 1 to min(# recs, # relevant items). You could say the version you found above is 'extended' to look into the long tail (# recs = # items), although the long tail doesn't affect MAP much. Same definition, different limit. precision@k does not have the same question since there is one k value, not lots. AUC may not help you if you're comparing to other things for which you don't have AUC. It was a side comment mostly. (Anyway there is already an AUC implementation here which I am trying to see if I can use.) > Add RankingMetrics to examples.MovieLensALS > ------------------------------------------- > > Key: SPARK-4231 > URL: https://issues.apache.org/jira/browse/SPARK-4231 > Project: Spark > Issue Type: Improvement > Components: Examples > Affects Versions: 1.2.0 > Reporter: Debasish Das > Fix For: 1.2.0 > > Original Estimate: 24h > Remaining Estimate: 24h > > examples.MovieLensALS computes RMSE for movielens dataset but after addition > of RankingMetrics and enhancements to ALS, it is critical to look at not only > the RMSE but also measures like prec@k and MAP. > In this JIRA we added RMSE and MAP computation for examples.MovieLensALS and > also added a flag that takes an input whether user/product recommendation is > being validated. > -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org