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From "ASF subversion and git services (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr
Date Thu, 08 Dec 2016 18:45:01 GMT

    [ https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15733037#comment-15733037
] 

ASF subversion and git services commented on SOLR-8542:
-------------------------------------------------------

Commit 9e8dd854cda6d56cc8d498cc23d138eeb74732fd in lucene-solr's branch refs/heads/branch_6x
from [~cpoerschke]
[ https://git-wip-us.apache.org/repos/asf?p=lucene-solr.git;h=9e8dd85 ]

SOLR-8542: master-to-branch_6x backport changes (Michael Nilsson, Naveen Santhapuri, Christine
Poerschke)

* removed 'boost' arg from LTRScoringQuery.createWeight signature
* classes extending Weight now implement normalize and getValueForNormalization
* FieldLengthFeatureScorer tweaks


> Integrate Learning to Rank into Solr
> ------------------------------------
>
>                 Key: SOLR-8542
>                 URL: https://issues.apache.org/jira/browse/SOLR-8542
>             Project: Solr
>          Issue Type: New Feature
>            Reporter: Joshua Pantony
>            Assignee: Christine Poerschke
>            Priority: Minor
>         Attachments: SOLR-8542-branch_5x.patch, SOLR-8542-trunk.patch, SOLR-8542.patch
>
>
> This is a ticket to integrate learning to rank machine learning models into Solr. Solr
Learning to Rank (LTR) provides a way for you to extract features directly inside Solr for
use in training a machine learned model. You can then deploy that model to Solr and use it
to rerank your top X search results. This concept was previously [presented by the authors
at Lucene/Solr Revolution 2015|http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-diego-ceccarelli-bloomberg-lp].
> [Read through the README|https://github.com/bloomberg/lucene-solr/tree/master-ltr-plugin-release/solr/contrib/ltr]
for a tutorial on using the plugin, in addition to how to train your own external model.



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