Return-Path: Delivered-To: apmail-lucene-java-user-archive@www.apache.org Received: (qmail 31122 invoked from network); 23 Aug 2006 19:44:38 -0000 Received: from hermes.apache.org (HELO mail.apache.org) (209.237.227.199) by minotaur.apache.org with SMTP; 23 Aug 2006 19:44:38 -0000 Received: (qmail 10662 invoked by uid 500); 23 Aug 2006 19:44:31 -0000 Delivered-To: apmail-lucene-java-user-archive@lucene.apache.org Received: (qmail 10635 invoked by uid 500); 23 Aug 2006 19:44:31 -0000 Mailing-List: contact java-user-help@lucene.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: java-user@lucene.apache.org Delivered-To: mailing list java-user@lucene.apache.org Received: (qmail 10624 invoked by uid 99); 23 Aug 2006 19:44:31 -0000 Received: from asf.osuosl.org (HELO asf.osuosl.org) (140.211.166.49) by apache.org (qpsmtpd/0.29) with ESMTP; Wed, 23 Aug 2006 12:44:31 -0700 X-ASF-Spam-Status: No, hits=0.0 required=10.0 tests= X-Spam-Check-By: apache.org Received-SPF: neutral (asf.osuosl.org: local policy) Received: from [169.229.70.167] (HELO rescomp.berkeley.edu) (169.229.70.167) by apache.org (qpsmtpd/0.29) with ESMTP; Wed, 23 Aug 2006 12:44:30 -0700 Received: by rescomp.berkeley.edu (Postfix, from userid 1007) id 2C88E5B779; Wed, 23 Aug 2006 12:44:03 -0700 (PDT) Received: from localhost (localhost [127.0.0.1]) by rescomp.berkeley.edu (Postfix) with ESMTP id 1F2FB7F403 for ; Wed, 23 Aug 2006 12:44:03 -0700 (PDT) Date: Wed, 23 Aug 2006 12:44:03 -0700 (PDT) From: Chris Hostetter To: java-user@lucene.apache.org Subject: Re: Scoring Technique based on Relevance Feeback & other Parameters In-Reply-To: <20060823123108.4D79110FB00B@asf.osuosl.org> Message-ID: References: <20060823123108.4D79110FB00B@asf.osuosl.org> MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII X-Virus-Checked: Checked by ClamAV on apache.org X-Spam-Rating: minotaur.apache.org 1.6.2 0/1000/N : 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 there is a distinction between storing more information about a term and storing additional information about a document. the flexible payload type approaches that have been discussed should make info about a term easy (ie: the term is "wind", it's type is "noun", it's usage in the sentence is as a "subject", it's importance is "88.3") but you can already store additional information about documents (like the total popularity of a document) in Lucene -- either by using the document boost (if you always want it to be part of the score calculations) or as a seperate field which you can factor into the score calculations using something like FunctionQuery... http://incubator.apache.org/solr/docs/api/org/apache/solr/search/function/package-summary.html ...i use this all the time to make "recent" docs score better, or "more popular docs" score better. -Hoss --------------------------------------------------------------------- To unsubscribe, e-mail: java-user-unsubscribe@lucene.apache.org For additional commands, e-mail: java-user-help@lucene.apache.org