Inline.

On Sat, Nov 11, 2017 at 6:31 PM, Pat Ferrel <[EMAIL PROTECTED]> wrote:
I don't think that this would actually help for most recommendation
purposes.

It might help to determine that some item or other has broken out of
historical rates. Thus, we might have "hotness" as a detected feature that
could be used as a boost at recommendation time. We might also have "not
hotness" as a negative boost feature.

Since we have a pretty good handle on the "other" counts, I don't think
that the Poisson test would help much with the cooccurrence stuff itself.

Changing the sampling rule could make a difference to temporality and would
be more like what Johannes is asking about.

And this works in the current regime. Simply add location tags to the user
histories and do cooccurrence against content. Locations will pop out as
indicators for some content and not for others. Then when somebody appears
in some location, their tags will retrieve localized content.

For localization based on strict geography, say for restaurant search, we
can just add business rules based on geo-search. A very large bank customer
of ours does that, for instance.

I think that this is a good approach.

But the (not) hotness feature might help with automated this.
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