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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Introducing randomness into my results
Date Sun, 03 Jul 2011 18:07:48 GMT
Whether recommendations is the highest volume source of new information or
not depends on the site.

Clearly you need other mechanisms like search and most popular and most
popular by genre and recently posted, but it is not unusual for these to be
completely insufficient.  The alternatives can see the similarity search,
but if recommendations are a dominant navigational means on the site then
these seeds will never come up high enough to be confirmed.

Another mechanism for this which is useful even in search is to give recent
items a boost.

Anti-flood doesn't actually help exploration that much except at a genre
level (or whatever means you pick).  You still have the problem of good
items being shadowed.

It is not unusual for dithering to cause a dramatic and almost immediate
boost in recommendation click-throughs.  THis probably has several
components including:

a) better and wider recommendations (but this happens on a longer time
scale)

b) the first page is no longer static so the user views recommendations as a
source of new information so they come back

c) given that the users are coming back to recommendations because of (b),
we are turning these return visits into (effectively) views of the second
and third pages of results.  Users don't tend to go to the next page even
when they see that the item at the bottom of the first page is still pretty
good.  Dithering brings that second page to them on the first page.

I commonly do the dithering based on a seed that is rounded down to the
nearest hour or so.  This gives a stable view on most refreshes, but then
shows new results pretty soon.  Users build fanciful models of what is
really happening, but it seems to engage them to have a predictable time
that "new" results will appear.

On Sun, Jul 3, 2011 at 2:43 AM, Sean Owen <srowen@gmail.com> wrote:

> On Sun, Jul 3, 2011 at 8:05 AM, Ted Dunning <ted.dunning@gmail.com> wrote:
> > For instance, if the recommendation engine recommends B if you have seen
> A
> > and there is little other way to discover C which is ranked rather low
> (and
> > thus never seen), then there is no way for the engine to even get
> training
> > data about C.  The fact is, however, that exploring the space around good
> > recommendations is a good thing to do.  This is referred to as the
> explore /
> > exploit trade-off in the multi-armed bandit literature.
>
> Agree, that's a good reason to mix it up. Recommendations are a
> secondary source of possible new user-item interactions (i.e. that is
> not the only way to discover C), but are far more productive at
> driving serendipity than just waiting for it to happen. See below... I
> guess I think of randomization as the crudest way to get this effect.
> Surely your "anti-flooding" is more directed and effective?

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