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From Gintautas Sulskus <gintautas.suls...@gmail.com>
Subject Re: Computing record score depending on its association with other records
Date Tue, 30 Jan 2018 17:26:22 GMT
Thanks, Alessandro, for your reply.

Indeed, LTR looks like what I need.

However, all of the LRT examples that I have found use a single collection
as a data source.
My data spans across two collections. Does LTR support this somehow or
should I 'denormalise' the data and merge both collections?
My concern is that the denormalisation will lead to a significant increase
in size on the drive.

Best,
Gintas


On Tue, Jan 30, 2018 at 2:30 PM, Alessandro Benedetti <a.benedetti@sease.io>
wrote:

> Hi Ginsul,
> let's try to wrap it up :
>
> 1) you have an item win N binary features ( given the fact that you
> represent the document with a list of feature Ids ( and no values) I would
> assume that it means that when the feature is in the list, it has a value
> of
> 1 for the item
>
> 2) you want to score (or maybe re-rank ? ) your documents giving the score
> you defined
>
> You could solve this problem with a number of possible customizations.
> Starting from an easy one, you could try to use the LTR re-ranker[1] .
>
> Specifically you can define your set of feature( and that should be
> possible
> using the component out of the box) and then a linear model ( and you
> already have the weights for the features so you don't need to train it).
>
> This can be close to what you want but you may want to customize a bit (
> given the fact that you may want to average the weight).
> For example you could define an extension of the linear model that does the
> average of the score ect ect...
>
>
> [1] https://lucene.apache.org/solr/guide/6_6/learning-to-rank.html
>
>
>
>
> -----
> ---------------
> Alessandro Benedetti
> Search Consultant, R&D Software Engineer, Director
> Sease Ltd. - www.sease.io
> --
> Sent from: http://lucene.472066.n3.nabble.com/Solr-User-f472068.html
>

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