Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/17090
Fitting into the CV / evaluator is actually fairly straightforward. It's
just that the semantics of `transform` for top-k recommendation must fit into
whatever we decide on for `RankingEvaluator`, so they are closely linked. (In
other words, they must be compatible). Once the semantics (basically output
schema for `transform`) are decided it's quite simple.
It was discussed on the JIRA
[here](https://issues.apache.org/jira/browse/SPARK-13857?focusedCommentId=15236796&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15236796)
and
[here](https://issues.apache.org/jira/browse/SPARK-13857?focusedCommentId=15822021&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15822021).
I haven't had a chance to refine it yet but I have a view on the best
approach now (basically to fit in with the design of
[SPARK-14409](https://issues.apache.org/jira/browse/SPARK-14409) and in
particular the basic version of #16618). I think that design / schema is more
"DataFrame-like".
In any event - I'm not against having the convenience methods for
recommend-all here. I support it. Ultimately the `transform` approach is mostly
for fitting into Pipelines & cross-validation. `transform` could call into
these convenience methods (though it will need a DataFrame-based input version).
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