Chris Hostetter wrote:
: It's got one difference from yours, in that the terms are allowed to
: occur in any order in the sub-phrases (so phrase "C B" from your
: original example is scored like "B C").
there's a much bigger differnece, in that your technique won't reqard
documents where B and C are "near" eachother, but A is farther away in the
document then the proximity value you calculate.
Daniel's goal is to make sure that documents matching any subphrase of the
orriginal query get a increase in score based on the length of hte
subphrase. in his specific example the orriginal query only had three
words, and he wanted all of them to be mandatory, but consider the case
where they are all optional. if i search for 'A B C Z', and Z is a
nonexistent term, he wants documents matching the phrase "A B C" to get
better scores then documents matching the phrase "A B", or "B C" which
should get better score then documents that just match the individual
terms with large gaps in between them.
your approach will still only increase the scores of documents where *all*
of the terms appear within some proximity.
-Hoss
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Thanks Robin for sharing your idea, your idea is certainly interesting,
but like Hoss says, I really wanted to be strict on what matches and
only boost documents with either the whole phrase or bits of sub-phrases
scored higher.
After much thought about testing a search engine, I came to the
conclusion that one of the best way of doing it would be to deploy it
locally for a few months and have our staff use it. When they do a
search, they are presented with two lists and they can choose which one
is the "best" (old version and new version). Better would be to just
never tell what list is generated by the newer version and see if in
most cases the newer version is prefered over the old one.
We just cant seem to find any pattern in a search log that would give us
any kind of certainty as to user satisfaction in a result, the bias is
always so large that it makes the whole statistical study almost useless.
Daniel
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