: I question whether such scores are more meaningful. Yes, such scores
: would be guaranteed to be between zero and one, but would 0.8 really be
: meaningful? I don't think so. Do you have pointers to research which
: demonstrates this? E.g., when such a scoring method is used, that
: thresholding by score is useful across queries?
I freely admit that I'm way out of my league on these scoring discussions,
but I believe what the OP was refering to was not any intrinsic benefit in
having a score between 0 and 1, but of having a uniform normalization of
scores regardless of search terms.
For example, using the current scoring equation, if i do a search for
"Doug Cutting" and the results/scores i get back are...
1: 0.9
2: 0.3
3: 0.21
4: 0.21
5: 0.1
...then there are at least two meaningful pieces of data I can glean:
a) document #1 is significantly better then the other results
b) document #3 and #4 are both equaly relevant to "Doug Cutting"
If I then do a search for "Chris Hostetter" and get back the following
results/scores...
9: 0.9
8: 0.3
7: 0.21
6: 0.21
5: 0.1
...then I can assume the same corrisponding information is true about my
new search term (#9 is significantly better, and #7/#8 are equally as good)
However, I *cannot* say either of the following:
x) document #9 is as relevant for "Chris Hostetter" as document #1 is
relevant to "Doug Cutting"
y) document #5 is equally relevant to both "Chris Hostetter" and
"Doug Cutting"
I think the OP is arguing that if the scoring algorithm was modified in
the way they suggested, then you would be able to make statements x & y.
If they are correct, then I for one can see a definite benefit in that.
If for no other reason then in making minimum score thresholds more
meaningful.
-Hoss
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