Thanks for the pointer.
I have been reading the code and trying to understand how to create an
efficient aggregate function but I must be missing something because it seems
to me that creating any kind of aggregation function which uses non primitive
types would have a high overhead.
Consider t
(cc'ing dev list also)
I think a more general version of ranking metrics that allows arbitrary
relevance scores could be useful. Ranking metrics are applicable to other
settings like search or other learning-to-rank use cases, so it should be a
little more generic than pure recommender settings.
