Hi,
I like to take the best n Documents per user which is stored as user_id in
my index. This wouldn't be a problem until now. It could be done like this:
{
"query":{
"match":{
"field":{
"query":"query_string"
}
}
},
"aggs":{
"group_by_user":{
"terms":{
"field":"user_id"
},
"aggs":{
"top_n":{
"top_hits":{
"size":10
}
}
}
}
}
}
But now I like to do a sub-aggregation on it to calculate some expensive
scoring and this isn't possible anymore, because top_hits is a metric
aggregation.
"aggs":{
"max_score_per_user":{
"max":{
"script":"advanced_scoring"
}
}
}
}
My scoring algorithm is very expensive, so I can't apply it on the full
document set per user which is returned by the query
I also can't use the rescore feature which provides a window parameter,
because I first have to bucket the documents per user and then take the
best n docs per user.
The range query would work, but the scoring aren't comparable because of
the IDF. So I can't define a fixed range.
So I either have to make the scoring results comparable, which would be
simple, but the constant_score query doesn't work with the match query
which I am using *or* I have to find a way to reduce the bucket size to a
certain limit while ordering by relevance.
I'm trying since days to find a way to do that, but it seems that it's not
possible.
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