Playing around with the query boosts and how they effect the results and 
the management wanted to know how the defaults were chosen.  We've seen 
in our testing that we can jerk the results around quite a bit and we 
could play with them until we are blue in the face to make our results 
look like the "big boys" so any guidance here would help....  So the 
questions are:

1. Who came up with them and were they developed against a large, 
real-world dataset?

2. If not, are there any best practice guidelines that people are using 
that seem to mimic real world results from commercial engines?

Tweakability is good, but real world performance is better and we just 
want to be sure our results are based on something more than my playing 
around with the values....

-- 
rp



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