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From Dennis Hendriksen <dennis.hendrik...@kalooga.com>
Subject RE: combine query score with external score
Date Mon, 01 Feb 2010 07:08:10 GMT
Hi Steve,

Thank you for your suggestions. Payloads might indeed help me to
overcome the precision loss problem that I am experiencing right now. I
don't think it will help me with the combining of Lucene scores with
external scores however.

Is there anyone who has a suggestion how to deal with that?

Dennis 

On Thu, 2010-01-28 at 13:52 -0500, Steven A Rowe wrote:
> Hi Dennis,
> 
> You should check out payloads (arbitrary per-index-term byte[] arrays), which can be
used to encode values which are then incorporated into documents' scores, by overriding Similarity.scorePayload():
> 
> <http://lucene.apache.org/java/3_0_0/api/core/org/apache/lucene/search/Similarity.html#scorePayload%28int,%20java.lang.String,%20int,%20int,%20byte[],%20int,%20int%29>
> 
> The Lucene in Action 2 MEAP has a nice introduction to using payloads to influence scoring,
in section 6.5.
> 
> See also this (slightly out-of-date*) blog post "Getting Started with Payloads" by Grant
Ingersoll at Lucid Imagination:
> 
> <http://www.lucidimagination.com/blog/2009/08/05/getting-started-with-payloads/>
> 
> *Note that since this blog post was written, BoostingTermQuery was renamed to PayloadTermQuery
(in Lucene 2.9.0+ ; see http://issues.apache.org/jira/browse/LUCENE-1827 ; wow - this issue
isn't mentioned in CHANGES.txt???):
> 
> <http://lucene.apache.org/java/3_0_0/api/core/org/apache/lucene/search/payloads/PayloadTermQuery.html>
> 
> Steve
> 
> On 01/28/2010 at 6:01 AM, Dennis Hendriksen wrote:
> > I'm struggling to create a performant query in Lucene 3.0.0 in which I
> > want to combine 'regular' scoring with scores derived from external
> > sources.
> > 
> > For each document a fixed set of scores is calculated in the range [0.0,
> > 1.0>. These scores represent the confidences that a document falls into
> > categories. So for example document #1 has a score of 0.3 for cat=boys,
> > 0.2 for cat=girls, 0.1 for cat=toys, 0.05 for cat=animals.
> > 
> > The 'regular' scoring is calculated using a BooleanQuery with TermQuerys
> > similar to: -type:H +(title:dna body:dna^1.5)
> > 
> > In the current naive approach I'm combining the scores as following: -
> > for each document store the three best categories in the following
> > fields:
> > name=cat1st value=boys fieldboost=0.3
> > name=cat2nd value=girls fieldboost=0.2
> > name=cat3rd value=toys fieldboost=0.1
> > Search-time use the following query if you're interested in 'girls':
> > -type:H +(title:dna body:dna^1.5) cat1st:girls cat2nd:girls cat3rd:girls 
> > or if you're interested in 'boys': 
> > -type:H +(title:dna body:dna^1.5) cat1st:boys cat2nd:boys cat3rd:boys
> > 
> > Disadvantages of the current approach:
> > - loss of precision encoding/decoding boosts (performance is important,
> > so this might be acceptable)
> > - using TermQuery for the cat fields doesn't make a lot of sense since
> > the external scores are multiplied by the idf of 'boys'/'girls' and
> > the querynorm
> > - the resulting score from the cat field is added to the other query
> > score instead of multiplied
> > 
> > Just to give you an idea: the index I'm using is growing in time and
> > contains about 50 million documents
> > 
> > Do you have an idea how I can improve my query and still keep high
> > performance? Or should I combine the scores in the Collector (but this
> > doesn't seem the right place to retrieve the category scores from the
> > index)? Is it possible to use a different float->byte encoder per field
> > to reduce the lack of precision?
> > 
> > Thanks for your time,
> > Dennis
> 
> 



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