I'm using elasticsearch to do some similarity comparison among process
models. The core similarity algorithm should be specialized for my process
models, which means, as my imagined, I should customize the score algorithm
As I known, the scoring in ES is based on Lucene score algorithm. Although
Lucene's DefaultSimilarity works quite well on most of the cases and one
can use other similarities in ES like BM25,DRF, such customizing usually
extending the existed Lucene classes or overriding its methods to change or
disable some weights in my opinion.
In my case, I'd like to make some specialized math things which should be
used as scoring and seems to be different with the base Lucene score
algorithm. what confused me is, it seems that there are two options for me,
one is, I can configurate custom scoring script in ES, the other one is I
should build my own Lucene scorer.
Can anybody give me some advice on which approach I should take? or is
there any missunderstanding I have. Since I have not very clear about ES
and Lucene, maybe there are some other ways to solve my questions and more
suitable in my case. Thanks a lot!
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