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 varying recommendation. As Johannes notes, this can make use of
windowed counts. The problem is that rarely accessed items should probably
have longer windows so that we use longer term trends when we have less
data.

The good news here is that this some part of this is nearly already in the
code. The trick is that the down-sampling used in the system can be adapted
to favor recent events over older ones. That means that if the meaning of
something changes over time, the system will catch on. Likewise, if
something appears out of nowhere, it will quickly train up. This handles
the popular in Greece right now problem.

But this isn't the whole story of changing recommendations. Another problem
that we commonly face is what I call the christmas music issue. The idea is
that there are lots of recommendations for music that are highly seasonal.
Thus, Bing Crosby fans want to hear White Christmas
<https://www.youtube.com/watch?v=P8Ozdqzjigg> until the day after christmas
at which point this becomes a really bad recommendation. To some degree,
this can be partially dealt with by using temporal tags as indicators, but
that doesn't really allow a recommendation to be completely shut down.

The only way that I have seen to deal with this in the past is with a
manually designed kill switch. As much as possible, we would tag the
obviously seasonal content and then add a filter to kill or downgrade that
content the moment it went out of fashion.

On Sat, Nov 11, 2017 at 9:43 AM, Johannes Schulte <
[EMAIL PROTECTED]> wrote:
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