Pat, thanks for your help. especially the insights on how you handle the
system in production and the tips for multiple acyclic buckets.
Doing the combination signalls when querying sounds okay but as you say,
it's always hard to find the right boosts without setting up some ltr
system. If there would be a way to use the hotness when calculating the
indicators for subpopulations it would be great., especially for a cross
recommender.

e.g. people in greece _now_ are viewing this show/product  whatever

And here the popularity of the recommended item in this subpopulation could
be overrseen when just looking at the overall derivatives of activity.

Maybe one could do multiple G-Tests using sliding windows
 * itemA&itemB  vs population (classic)
 * itemA&itemB(t) vs itemA&itemB(t-1)
..

and derive multiple indicators per item to be indexed.

But this all relies on discretizing time into buckets and not looking at
the distribution of time between events like in presentation above - maybe
there is  something way smarter

Johannes

On Sat, Nov 11, 2017 at 2:50 AM, Pat Ferrel <[EMAIL PROTECTED]> wrote:
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