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From "Yuki Yano (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SOLR-11302) Flexible LTR model which uses DSL for describing the scoring function
Date Thu, 31 Aug 2017 07:57:00 GMT
Yuki Yano created SOLR-11302:
--------------------------------

             Summary: Flexible LTR model which uses DSL for describing the scoring function
                 Key: SOLR-11302
                 URL: https://issues.apache.org/jira/browse/SOLR-11302
             Project: Solr
          Issue Type: Improvement
      Security Level: Public (Default Security Level. Issues are Public)
          Components: contrib - LTR
            Reporter: Yuki Yano
            Priority: Minor
         Attachments: SOLR-11302_master.patch

We implment new LTR model which uses DSL for describing the scoring function.
This model is inspired by the implementation of {{JavascriptCompiler}} which compiles javascript
codes by using ANTLR and ASM.
The syntax of our DSL is based on Java and FunctionQuery, and we can describe various models
freely by using the DSL.
Please see {{ltr/dsl/package-info.java}} for more details.

The configuration of our model looks like belows:

{code}
{
  "class":"org.apache.solr.ltr.model.DSLModel",
  "name":"dslmodel",
  "features":[
    {"name":"popularity"},
    {"name":"normHits"},
    {"name":"isTrendy"}
  ],
  "params":{
    "dsl": "(0.2 * popularity / 10 + 0.3 * normHits) * if(isTrendy==1, 2.0, 1.0)"
  }
}
{code}

This approach is similar to re-ranking with FunctionQuery, except that our model compiles
the DSL into bytecodes on ahead and can calculate scores faster.
In practice, the performance of the model is depended on the structure of formula.
For example, if we convert existing models (i.e., {{LinearModel}} and {{MultipleAdditiveTreesModel}})
to DSLs and compare the performance (with large models),
our model is 4.5x slower than {{LinearModel}} but 1.5x faster than {{MultipleAdditiveTreesModel}}.



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