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From Ryan Yacyshyn <ryan.yacys...@gmail.com>
Subject Re: When to use LTR
Date Thu, 22 Jun 2017 13:33:48 GMT
Thanks for your help Alessandro!

Ryan


On Wed, 21 Jun 2017 at 19:25 alessandro.benedetti <a.benedetti@sease.io>
wrote:

> 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
> --
> View this message in context:
> http://lucene.472066.n3.nabble.com/When-to-use-LTR-tp4342130p4342140.html
> Sent from the Solr - User mailing list archive at Nabble.com.
>

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