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From Pat Ferrel <>
Subject Re: Negative Preferences in a Recommender
Date Tue, 18 Jun 2013 15:07:57 GMT
They are on a lot of papers, which are you looking at?

On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov <> wrote:

(Kinda doing something very close. )

Koren-Volynsky paper on implicit feedback can be generalized to decompose
all input into preference (0 or 1) and confidence matrices (which is
essentually an observation weight matrix).

If you did not get any observations, you encode it as (p=0,c=1) but if you
know that user did not like item, you can encode that observation with much
more confidence weight, something like (p=0, c=30) -- actually as high
confidence as a conversion in your case it seems.

The problem with this is that you end up with quite a bunch of additional
parameters in your model to figure, i.e. confidence weights for each type
of action in the system. You can establish that thru extensive
crossvalidation search, which is initially quite expensive (even for
distributed machine cluster tech), but could be incrementally bail out much
sooner after previous good guess is already known.

MR doesn't work well for this though since it requires  A LOT of iterations.

On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel <> wrote:

> In the case where you know a user did not like an item, how should the
> information be treated in a recommender? Normally for retail
> recommendations you have an implicit 1 for a purchase and no value
> otherwise. But what if you knew the user did not like an item? Maybe you
> have records of "I want my money back for this junk" reactions.
> You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no
> value as usual means no preference? Some of the math here won't work though
> since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no
> preference implicitly = 0?
> Would it be better to treat the bad rating as a 1 and good as 2? This
> would be more like the old star rating method only we would know where the
> cutoff should be between a good review and bad (1.5)
> I suppose this could also be treated as another recommender in an ensemble
> where r = r_p - r_h, where r_h = predictions from "I hate this product"
> preferences?
> Has anyone found a good method?

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