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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr
Date Sat, 16 Jan 2016 00:08:40 GMT

    [ https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15102741#comment-15102741
] 

ASF GitHub Bot commented on SOLR-8542:
--------------------------------------

GitHub user diegoceccarelli opened a pull request:

    https://github.com/apache/lucene-solr/pull/217

    SOLR-8542: Integrate Learning to Rank into Solr

    See https://issues.apache.org/jira/i#browse/SOLR-8542

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/bloomberg/lucene-solr trunk-learning-to-rank-plugin

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/lucene-solr/pull/217.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #217
    
----
commit 336db4ccf6434e690a745a4af88b5d9c21edc25e
Author: Diego Ceccarelli <dceccarelli4@bloomberg.net>
Date:   2016-01-13T22:29:17Z

    SOLR-8542: Integrate Learning to Rank 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

----


> 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: README.md, SOLR-8542-branch_5x.patch, SOLR-8542-trunk.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
).
> The attached code was jointly worked on by Joshua Pantony, Michael Nilsson, and Diego
Ceccarelli.
> Any chance this could make it into a 5x release? We've also attached documentation as
a github MD file, but are happy to convert to a desired format.
> h3. Test the plugin with solr/example/techproducts in 6 steps
> Solr provides some simple example of indices. In order to test the plugin with 
> the techproducts example please follow these steps
> h4. 1. compile solr and the examples 
> cd solr
> ant dist
> ant example
> h4. 2. run the example
> ./bin/solr -e techproducts 
> h4. 3. stop it and install the plugin:
>    
> ./bin/solr stop
> mkdir example/techproducts/solr/techproducts/lib
> cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar example/techproducts/solr/techproducts/lib/
> cp contrib/ltr/example/solrconfig.xml example/techproducts/solr/techproducts/conf/
> h4. 4. run the example again
>     
> ./bin/solr -e techproducts
> h4. 5. index some features and a model
> curl -XPUT 'http://localhost:8983/solr/techproducts/schema/fstore'  --data-binary "@./contrib/ltr/example/techproducts-features.json"
 -H 'Content-type:application/json'
> curl -XPUT 'http://localhost:8983/solr/techproducts/schema/mstore'  --data-binary "@./contrib/ltr/example/techproducts-model.json"
 -H 'Content-type:application/json'
> h4. 6. have fun !
> *access to the default feature store*
> http://localhost:8983/solr/techproducts/schema/fstore/_DEFAULT_ 
> *access to the model store*
> http://localhost:8983/solr/techproducts/schema/mstore
> *perform a query using the model, and retrieve the features*
> http://localhost:8983/solr/techproducts/query?indent=on&q=test&wt=json&rq={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}&fl=*,[features],price,score,name&fv=true



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