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From "alessandro.benedetti" <a.benede...@sease.io>
Subject Re: When to use LTR
Date Wed, 21 Jun 2017 11:25:33 GMT
Hi Ryan,
first thing to know is that Learning To Rank is about relevancy and
specifically it is about to improve your relevancy function.
Deciding if to use or not LTR has nothing to do with your index size or
update frequency ( although LTR brings some performance consideration you
will need to evaluate) .

Functionally, the moment you realize you want LTR is when you start tuning
your relevancy.
Normally the first approach is the manual one, you identify a set of
features, interesting for your use case and you tune a boosting function to
improve your search experience.

e.g.
you decide to weight more the title field than the content and then boosting
recent documents.

What happens next is : 
"How much should I weight more the title ?"
"How much should I boost recent documents ?"

Normally you just check some golden queries and you try to manually optimise
these boosting factors by hand.

LTR answers to this requirements.
To make it simple LTR will bring you a model that will tell you the best
weighting factors given your domain ( and past experience) to get the most
relevant results for all the queries ( this is the ideal, of course it is
quite complicated and it depends of a lot of factors)

Of course it doesn't work like magic and you will need to extensively design
your features ( features engineering), build a valid training set ( explicit
or implicit), decide the model that best suites your needs ( linear model or
Tree based ?) and a lot of corollary configurations.

hope this helps!





-----
---------------
Alessandro Benedetti
Search Consultant, R&D Software Engineer, Director
Sease Ltd. - www.sease.io
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