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https://issues.apache.org/jira/browse/LUCENE-1910?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12757924#action_12757924
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Mark Harwood commented on LUCENE-1910:
--------------------------------------
Hi Thomas,
Following your request for feedback, some initial thoughts from a very quick
look.
* The "Information Gain" algo could use a little more explanation e.g. using
variable names other than "num1" and "num2" and could perhaps be extracted into
a utility class
* Is this scalable? It looks like in initialize it is loading this:
{code:title=MoreLikeThisUsingTags.java|borderStyle=solid}
/**
* All terms in the index
*/
protected HashSet docTerms=new HashSet();
{code}
..that seems a little scary!
It's also doing a seperate BooleanQuery for all items in this list ( and
repeated for >1 tag?). Thats look like a lot of searches.
I need to spend a little more time looking at it before I understand it in more
detail.
Before then - have you tested this on a big (millions of docs/terms) index?
Some performance figures would be useful to accompany this.
Cheers,
Mark
> Extension to MoreLikeThis to use tag information
> ------------------------------------------------
>
> Key: LUCENE-1910
> URL: https://issues.apache.org/jira/browse/LUCENE-1910
> Project: Lucene - Java
> Issue Type: New Feature
> Components: contrib/*
> Reporter: Thomas D'Silva
> Priority: Minor
> Attachments: LUCENE-1910.patch
>
>
> I would like to contribute a class based on the MoreLikeThis class in
> contrib/queries that generates a query based on the tags associated
> with a document. The class assumes that documents are tagged with a
> set of tags (which are stored in the index in a seperate Field). The
> class determines the top document terms associated with a given tag
> using the information gain metric.
> While generating a MoreLikeThis query for a document the tags
> associated with document are used to determine the terms in the query.
> This class is useful for finding similar documents to a document that
> does not have many relevant terms but was tagged.
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