Hi,
I'm working on literature texts (French).
My users are interested in relevance tweaking to have the most suggested
texts (for their taste) in top results.
Change similarity at query time is less expensive than reindex all.
I checked that BM25 needs to write “norms“ to keep document length.
Have I missed something ? DFISimilarity seems to write and use norms
from SimilarityBase, where it is written
computeNorms «Encodes the document length in the same way as {@link
BM25Similarity}»
For my first experiences, it seems that results with DFISimilarity at
query time are the same with an index encoded with default
BM25Similarity or DFI.
Can some gurus confirm with their experience ?
Thanks in advance (and lucene is really a good piece of software).
--
Frédéric
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