Hopefully I'm not opening myself up to public ridicule with what may be a very stupid question, but...
At the moment, I'm trying to wrap my head around some of the math that happens when Lucene does scoring. Let's put aside the big equation for a moment and focus on a simple method, such as tf(). [term frequency] I understand that tf(freq) is supposed to return larger values when freq is large, and smaller values when freq is small. Though here's what making me scratch my head today: a) Where does freq come from? (Not what is it, but who computes it and how?) Reason I ask is: b) How do I know what "large" and "small" is, as I don't really have a relative scale of what the max and min values are? Should I just assume a linear scale of 1.0 to 0.0 will be passed to the method? But then that begs the question... c) What values should I be passing out of a function like this? Should I normalize my outgoing scores to some scale, or do I simply just need to provide numbers that "have the right shaped curve". I wish the documentation shed a smidgen bit more light in those areas. I look at things like idf() which returns 1+log(ratio) and then has that value squared. Clearly that isn't on a scale of 1.0 to 0.0. I feel like there may be some mathematical trickery going on and that maybe the actual score values themselves don't matter inside the ranking code, so long as their relative values to one another. This then makes me ponder how the normalization process is done between queries, allowing for a mix'n'match of results as these numbers spill to the outside world. Obviously normalization has to happen at that point for the mixing query results magic to work. Is there a math wizard in the group who can talk to me like I'm four years old? -wls http://www.wwco.com/~wls/blog/ --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
