Hi,
I use Lucene to index user's documents. I have a potential of 2 or more
millions users so that i think a per-user index will not be a scalable
solution. All my searches are filtered with a user UID field.
As far as i know the default similarity calculate Inverse Document
Frequency as follow:
Math.log(numDocs/(double)(docFreq+1)) + 1.0)
where numDocs stands for the number of documents within the whole
collection and docFreq for the number of times Term t appear in the
whole collection.
My problem here is that this formula seems not to be reliable for my
system because numDocs should correspond to the number of documents in
the user's collection and docFreq for the number of times the Term T
appears in the user's collection.
Because Terms are stored as a single token i was thinking of
concatenating terms with a UID in order to separate them because :
Term "car" for user1 is different to term "car" for user2. My solution
would index "carUSERUID1" "carUSERUID2".
What would you suggest ?
Regards,
Lionel
