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From "Yuki Yano (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SOLR-11302) Flexible LTR model which uses DSL for describing the scoring function
Date Thu, 31 Aug 2017 07:57:01 GMT

     [ https://issues.apache.org/jira/browse/SOLR-11302?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Yuki Yano updated SOLR-11302:
-----------------------------
    Attachment: SOLR-11302_master.patch

> 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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