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https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15105790#comment-15105790
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Christine Poerschke commented on SOLR-8542:
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Hi Diego - thanks for patch update:
* I started looking at the code in
https://github.com/apache/lucene-solr/pull/217.patch today but am still a bit
undecided on how to best share comments, e.g. reviews.apache.org vs. github
pull request comments vs. in this JIRA log vs. some other way? Code comments in
this JIRA log would probably distract from what the new feature is about.
reviews.apache.org seems to have a nicer diff than github but does it require
extra step(s) after updating the github pull request (I have not used
reviews.apache.org so far).
* ticket cross-reference: LUCENE-6971 removed StorableField and StoredDocument
yesterday/today (217.patch from the day-before-yesterday used them in a few
places)
> 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, 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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