;>>> and user-locale. This would yield personal recs preferred in the
> > user’s
> >>>> locale. Athens-west-side in this case.
> >>>>
> >>>
> >>> And this works in the current regime. Simply add location tags to the
> >> us
ore like what Johannes is asking about.
> > >>>
> > >>>
> > >>>> But it doesn’t relate to popularity as I think Ted is saying.
> > >>>>
> > >>>> Are you looking for 1) personal recommendations biased by hotness in
&g
would be
> >> user-id,
> >>>> and user-locale. This would yield personal recs preferred in the
> > user’s
> >>>> locale. Athens-west-side in this case.
> >>>>
> >>>
> >>> And this works in the current regime. Simply add lo
s 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
t;
> > > 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.
> > >
> > >
> > > >
ld have no user-id since it is not personalized but would yield “hot
> > in
> > > Greece”
> > >
> >
> > I think that this is a good approach.
> >
> >
> > >
> > > Ted’s “Christmas video” tag is what I was calling a business rule and
>
is not personalized but would yield “hot
> in
> > Greece”
> >
>
> I think that this is a good approach.
>
>
> >
> > Ted’s “Christmas video” tag is what I was calling a business rule and can
> > be added to either of the above techniques.
> >
>
> But the (not) hotnes
>
> On Nov 11, 2017, at 4:01 AM, Ted Dunning <ted.dunn...@gmail.com> wrote:
>
> So ... there are a few different threads here.
>
> 1) LLR but with time. Quite possible, but not really what Johannes is
> talking about, I think. See http://bit.ly/poisson-llr for a quick
> d
techniques.
On Nov 11, 2017, at 4:01 AM, Ted Dunning <ted.dunn...@gmail.com> wrote:
So ... there are a few different threads here.
1) LLR but with time. Quite possible, but not really what Johannes is
talking about, I think. See http://bit.ly/poisson-llr for a quick
discussion.
2) time v
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
BTW you should take time buckets that are relatively free of daily cycles like
3 day, week, or month buckets for “hot”. This is to remove cyclical affects
from the frequencies as much as possible since you need 3 buckets to see the
change in change, 2 for the change, and 1 for the event volume.
So your idea is to find anomalies in event frequencies to detect “hot” items?
Interesting, maybe Ted will chime in.
What I do is take the frequency, first, and second, derivatives as measures of
popularity, increasing popularity, and increasingly increasing popularity. Put
another way popular,
Hi "all",
I am wondering what would be the best way to incorporate event time
information into the calculation of the G-Test.
There is a claim here
https://de.slideshare.net/tdunning/finding-changes-in-real-data
saying "Time aware variant of G-Test is possible"
I remember i experimented with
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