Github user mpjlu commented on the issue:
https://github.com/apache/spark/pull/17919
Hi @auskalia , you are right. repartition can improve the performance of
recommendForAll.
In my experiment for PR 17742, I have 120 cores, I use 20 partition for
userFeatures, and itemFeatures.
I also consider to provide interface to user to have a chance to do
re-partition.
Since you can set the partition number when train the model, I did not do
that.
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