Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-62064318
@coderxiang I read the reference paper and I understood the issue...
I thought it as regression metric before but it is not...the predicted
value does not matter...the rank of the movieId from predicted set matters...I
am updating the PR with following steps (this is focused on user
recommendation) if the --validateRecommedation is set...
1. For every user generate train and test set using (0.8, 0.2) and use
RDD.sampleByKey
2. For every user, the predicted set is of size numProducts...I am using
MatrixFactorizationModel.recommendProduct(userId, numProducts) API to generate
the predicted set
3. For every user, the labeled set comes from the test set as computed in
Step 1
4. Once I have these two array for every user, I call RankingMetrics to
call meanAveragePrecision
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