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https://issues.apache.org/jira/browse/SOLR-11302?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yuki Yano updated SOLR-11302:
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Issue Type: New Feature (was: Improvement)
> 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: New Feature
> 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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