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Date: Sun, 13 Nov 2005 10:10:22 +0100
From: Sebastian Marius Kirsch <skirsch@sebastian-kirsch.org>
To: java-user@lucene.apache.org
Subject: Re: About Combining Scores
Message-ID: <20051113091022.GS1399@amok.local>
References: <17540.1131836681@www91.gmx.net>
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On Sun, Nov 13, 2005 at 12:04:41AM +0100, Karl Koch wrote:
> My aim is to combine this two scores. The Lucenes score is normalisied
> between 0.0 and 1.0 (if the score exceeded 1.0 at some point) or less then
> 1.0 (if it did not). The user model looks the same in this perspective -
> although based on different data - a 1.0 means the maximum of relevance and
> a 0.0 a minimum or relevance. At the moment I am multiplying the Lucene
> score with the score produced by the user model. This means the resulting,
> combiend socre is number between 0.0 and 1.0 and represents the merged view
> from both models - the IR view and the view of the user model.

I came across that question too recently; it seems to be a rather
under-researched topic in the literature. I used multiplication in the
end, because it's simple, it produces reasonable results, it's not
tunable, and it's invariant to normalization. (Don't make a model with
tunable parameters if you don't know how to tune them ...)

The most helpful paper I came across was this:

http://trec.nist.gov/pubs/trec13/papers/microsoft-cambridge.web.hard.pdf

It's about combining PageRank with a relevance score, but it contains
a good description of how they arrived at their scoring formula. They
use a linear combination of the two measures and transform them to
have a roughly similar distribution. They then tuned the parameters
using a test corpus (which may be difficult/impossible for your
application.) Their system was one of the best at TREC-13.

Regards, Sebastian

-- 
Sebastian Kirsch <skirsch@sebastian-kirsch.org> [http://www.sebastian-kirsch.org/]

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