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From "Russell M. Allen" <Russell.Al...@aebn.net>
Subject RE: Scoring Technique based on Relevance Feeback & other Parameters
Date Wed, 23 Aug 2006 14:29:43 GMT
I have a similar interest in specifying a custom scoring strategy.  I
previously posted about it under the subject "Scoring a document
(count?)" on 7/27/06.  In brief, I want a documents score to be a count
of term matches.  This is nearly identical to a SQL count()
functionality.
 
If you are able to modify Lucene such that I can specify a weight and
scorer as the calling code then, yes, I am definitely interested.
 
 

________________________________

From: sachin [mailto:sachin.khaire@noemacorp.com] 
Sent: Wednesday, August 23, 2006 8:31 AM
To: java-user@lucene.apache.org
Subject: Scoring Technique based on Relevance Feeback & other Parameters


 

Hello Great/smart guys 

       This is my first question for this group as I started working on
the Lucene last month. 

 

        Lucene provide the scoring of documents based on TF-IDF vector
analysis. Lucene also provides the Scorer and Weight inside the Search
package. By implementing new type of  tuple (Query,Weight,Scorer) I can
easily implement new Scoring technique. Unfortunatly Lucene index shows
that it stores only TF / Position vectors for each term within document.


 

        I am interested in investigating new scoring technique where I
will use some other parameters relating to the Term to rank the
documents. For an example web page ranking is assisted by parameters
like number of links towards webpage and number of link from web - page.
It indicates that we need to store relatively more information about
terms within the index. But HoW ? ... I need to investigate

 

        Another parameter is relevance feedback from the User. Ranking
should get affected by relevance feedback from the user. 

 

Would someone interested in helping out or thinking about the same
problem.


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