[
https://issues.apache.org/jira/browse/SOLR-11302?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yuki Yano updated SOLR-11302:
-----------------------------
Description:
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}}.
was:
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}}.
> 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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