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https://issues.apache.org/jira/browse/LUCENE-8563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16683815#comment-16683815
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Elizabeth Haubert commented on LUCENE-8563:
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Mathematically, it changes the ratio of
{code:java}
tf * idf / ( tf + norm)
{/code}
which determines the relative importance of the norms parameter. It seems
like that should affect ranking, at least for low values of tf. Why not just
set the parameter to 0 for the cases you are looking at?
> 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
>
> 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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