[jira] [Comment Edited] (SOLR-8542) Integrate Learning to Rank into Solr

["Diego Ceccarelli (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15184848#comment-15184848 ] 

Diego Ceccarelli edited comment on SOLR-8542 at 3/8/16 12:35 PM:
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we decided to decouple models and features because a) the general use case is that you use a particular model (+ relying on a set of features) to rank your documents, but you also want to compute (and log) new features for training a new model to use in the future. All the features in a feature store will be computed but the model will receive only the requested features (allowing also to update the feature store adding new features without affecting the model) b) two models could use the same feature, but normalize the feature values in a different way (see the {{Normalizer
 class) 


was (Author: diegoceccarelli):
we decided to decouple models and features because a) the general use case is that you use a particular model (+ relying on a set of features) to rank your documents, but you also want to compute (and log) new features for training a new model to use in the future. All the features in a feature store will be computed but the model will receive only the requested features (allowing also to update the feature store adding new features without affecting the model) b) two models could use the same feature, but normalize the feature values in a different way (see the {Normalizer} class}) 

> 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, David Grohmann 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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