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https://issues.apache.org/jira/browse/LUCENE-8563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16704438#comment-16704438
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ASF subversion and git services commented on LUCENE-8563:
---------------------------------------------------------

Commit cf016f8987e804bcd858a2a414eacdf1b3c54cf5 in lucene-solr's branch 
refs/heads/master from javanna
[ https://git-wip-us.apache.org/repos/asf?p=lucene-solr.git;h=cf016f8 ]

LUCENE-8563: Remove k1+1 constant factor from BM25 formula numerator.

Signed-off-by: Adrien Grand <[email protected]>


> Remove k1+1 from the numerator of  BM25Similarity
> -------------------------------------------------
>
>                 Key: LUCENE-8563
>                 URL: https://issues.apache.org/jira/browse/LUCENE-8563
>             Project: Lucene - Core
>          Issue Type: Improvement
>            Reporter: Adrien Grand
>            Priority: Minor
>          Time Spent: 40m
>  Remaining Estimate: 0h
>
> Our current implementation of BM25 does
> {code:java}
> boost * IDF * (k1+1) * tf / (tf + norm)
> {code}
> As (k1+1) is a constant, it is the same for every term and doesn't modify 
> ordering. It is often omitted and I found out that the "The Probabilistic 
> Relevance Framework: BM25 and Beyond" paper by Robertson (BM25's author) and 
> Zaragova even describes adding (k1+1) to the numerator as a variant whose 
> benefit is to be more comparable with Robertson/Sparck-Jones weighting, which 
> we don't care about.
> {quote}A common variant is to add a (k1 + 1) component to the
>  numerator of the saturation function. This is the same for all
>  terms, and therefore does not affect the ranking produced.
>  The reason for including it was to make the final formula
>  more compatible with the RSJ weight used on its own
> {quote}
> Should we remove it from BM25Similarity as well?
> A side-effect that I'm interested in is that integrating other score 
> contributions (eg. via oal.document.FeatureField) would be a bit easier to 
> reason about. For instance a weight of 3 in FeatureField#newSaturationQuery 
> would have a similar impact as a term whose IDF is 3 (and thus docFreq ~= 5%) 
> rather than a term whose IDF is 3/(k1 + 1).



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