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From "adeppa (JIRA)" <>
Subject [jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr
Date Tue, 06 Dec 2016 11:11:23 GMT


adeppa commented on SOLR-8542:

Hi Team,

I am working on LTR master branch with solr 6.3, when i try to integrate code in eclipse showing
to me compile time errors in couple of class i.e FieldLengthFeatureWeight,LTRScoringQuery
,After adding the unimplemented  methods to couple of class i.e FieldValueFeatureWeight,SolrFeatureWeight,ValueFeatureWeight

In the LTRScoringQuery class showing error on  
  public ModelWeight createWeight(IndexSearcher searcher, boolean needsScores, float boost)
      throws IOException 

Note :if i remove @Override  error is went off ,is it any impact 

and FieldLengthFeatureWeight class showing error on
public FieldLengthFeatureScorer(FeatureWeight weight,
          NumericDocValues norms) throws IOException {
        super(weight, norms);

Note : Here super (weight,norms ) method showing error 
      public float score() throws IOException {

        final long l = norms.longValue();
Note : norms.longValue(); statement is showing error 
please help me for the above error resolution  


> Integrate Learning to Rank into Solr
> ------------------------------------
>                 Key: SOLR-8542
>                 URL:
>             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|].
> [Read through the README|]
for a tutorial on using the plugin, in addition to how to train your own external model.

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