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From Doug Cutting <>
Subject Re: Similarity score computation documentation
Date Tue, 14 Sep 2004 21:49:00 GMT
Your analysis sounds correct.

At base, a weight is a normalized tf*idf.  So a document weight is:

   docTf * idf * docNorm

and a query weight is:

   queryTf * idf * queryNorm

where queryTf is always one.

So the product of these is (docTf * idf * docNorm) * (idf * queryNorm), 
which indeed contains idf twice.  I think the best documentation fix 
would be to add another idf(t) clause at the end of the formula, next to 
queryNorm(q), so this is clear.  Does that sound right to you?


Ken McCracken wrote:
> Hi,
> I was looking through the score computation when running search, and
> think there may be a discrepancy between what is _documented_ in the
> class overview Javadocs, and what
> actually occurs in the code.
> I believe the problem is only with the documentation.
> I'm pretty sure that there should be an idf^2 in the sum.  Look at
>, the inner class TermWeight.  You
> can see that first sumOfSquaredWeights() is called, followed by
> normalize(), during search.  Further, the resulting value stored in
> the field "value" is set as the "weightValue" on the TermScorer.
> If we look at what happens to TermWeight, sumOfSquaredWeights() sets
> "queryWeight" to idf * boost.  During normalize(), "queryWeight" is
> multiplied by the query norm, and "value" is set to queryWeight * idf
> == idf * boost * query norm * idf == idf^2 * boost * query norm.  This
> becomes the "weightValue" in the TermScorer that is then used to
> multiply with the appropriate tf, etc., values.
> The remaining terms in the Similarity description are properly
> appended.  I also see that the queryNorm effectively "cancels out"
> (dimensionally, since it is a 1/ square root of a sum of squares of
> idfs) one of the idfs, so the formula still ends up being roughly a
> TF-IDF formula.  But the idf^2 should still be there, along with the
> expansion of queryNorm.
> Am I mistaken, or is the documentation off?
> Thanks for your help,
> -Ken
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