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From Sean Owen <>
Subject Re: Inconsistent recommendations
Date Wed, 03 Jun 2009 20:17:23 GMT
Yeah that's what it's about -- skipping some randomly-selected stuff,
which changes the input to the calculations and potentially changes
its output. It's about speed. It doesn't 'permanently' omit data -- if
I threw out half the users, sure things would be deterministic then
but half the users would be pretty unhappy... similar arguments mean
we have to keep all the data and ignore parts of it selectively in
different contexts.

Unless the variation is problematic, I wouldn't raise the sampling
rate. It's only if it gets too weird you might consider it.  If you
get no recommendations but have reason to believe you should, you
could always query again too.

PS first name is Sean...

On Wed, Jun 3, 2009 at 9:00 PM, Otis Gospodnetic
<> wrote:
> I see.  I thought sampling rate was only about providing a way to skip some input records
(user, item, preference tuples) to lower memory requirements and increase speed.  I didn't
realize it could affect recommendation computation...
> 3) is definitely needed, at least in my case, and that's what I do.  Big time. :)
> 2) is also good to know - if different sets of recommended items all look good (i.e.
really do feel like good recommendations) to users, this adds variety, and I feel that can
be a good thing, at least in my current domain.
> So I suppose I simply can't have the sampling rate too low.  Thanks Owen.

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