: Regardless of the semantics, it doesn't sound like DF would give you what you : want. It could be entirely possible that in some short timespan the number of : docs on Iran could match up w/ the number on Obama (maybe not for that : particular example) in which case your "hot" item would no longer appear hot.
but if hte numbers match up in that timespan then the "hot" item isn't as "hot" anymore. Myabe i'm missunderstanding: but it sounds like Asif's question esentailly boils down to getting facet constraints sorted after using some normalizing fraction ... the simplest case being the inverse ratio (this is where i think Asif is comparing it to IDF) of the number of matches for that facet in some larger docset to the size of the docset-- typically that docset could be the entire index, but it could also be the same search over a large window of time. So if i was doing a news search for all docs in the last 24 hours, I could multiple each of those facet counts by the ratio of the corrisponding counts from the past month to the number of articles from the past monght see how much "hotter" they are in my smaller result set... current result set facet counts (X)... News:1100 Obama:1000 Iran:800 Miley Cyrus:700 iPod:500 facet counts from the past month (Y), during which type 9000 (Z) documents were published... News:9000 Obama:7000 Iran:1000 Miley Cyrus:4000 iPod:5000 X*(Z/Y)... Iran:7200 Miley Cyrus:1575 Obama:1285.7 News:1100 iPod:900 Doing this in a Solr plugin would be the best way to to this -- because otherwise your "hot" terms might not even show up in the facet lists. any attempt to do it on the client would just be an approximation, and could easily miss the "hottest" item if it was just below cutoff for hte number of constraints to be returned. -Hoss
