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From "Graham Poulter (JIRA)" <j...@apache.org>
Subject [jira] Created: (SOLR-1599) Improve IDF and relevance by separately indexing different entity types sharing a common schema
Date Wed, 25 Nov 2009 06:27:39 GMT
Improve IDF and relevance by separately indexing different entity types sharing a common schema
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                 Key: SOLR-1599
                 URL: https://issues.apache.org/jira/browse/SOLR-1599
             Project: Solr
          Issue Type: New Feature
          Components: Schema and Analysis
            Reporter: Graham Poulter


In Solr 1.4, the IDF (Inverse Document Frequency) is calculated on all of the documents in
an index.  This introduces relevance problems when using a single schema to store multiple
entity types, for example to support "search for tracks" and "search for artists".   The ranking
for search on the _name_ field of _track_ entities will be (much?) more accurate if the IDF
for the name field does not include counts from _artist_ entities.  The effect on ranking
would be most pronounced for query terms that have a low document frequency for _track_ entities
but a high frequency for _artist_ entities.

The current work-around to make the IDF be entity-specific is to use a separate Solr core
for each entity type sharing the schema - and repeating the process of copying solrconfig.xml
and schema.xml to all the cores.  This would be more complicated with replication, and even
more complicated with index distribution, because you must now maintain a core for _artists_
and a core for _tracks_ on each node.

David Smiley, author of _Solr 1.4 Enterprise Search Server", has filed SOLR-1158, where he
suggests calculating _numDocs_ after the application of filters.  However, _numDocs_ is just
the total number of documents: the document frequency (DF) for a query term of a _track_ search
would also need to exclude _artist_ entities from the DF_t total to get the IDF_t=log(N/DF_t).
 However, DF_t needs to be calculated at index time, when Solr has no idea what filters will
be applied.

I suggest using a metadata field _entitytype_ to specified on submitting a batch of documents,
with a configured list of allowed values: in the example the document could specify either
entitytype="track" or entitytype="artist" (defaulting to _track_).  The document frequency
would then calculated for each entity type during indexing. so for term "foo" there will be
two DF's stored: the DF of "foo" for entitytype="artist" and the DF of "foo" for entitytype="track".
  This might be implemented by instantiating a separate Lucene index for each configured entity
type.  Filtering on entitytype="artist" would then be implemented by searching only the _artist_
index.  

With this solution (entity type metadata field implemented with separate Lucene indeces) a
single Solr core can support many different entity types that share a common schema but use
partially overlapping subsets of fields, instead of having to configure maintain, replicate
and distribute for every entity type.

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