On Tue, Jun 18, 2013 at 3:48 AM, Ted Dunning wrote:
> I have found that in practice, don't-like is very close to like. That is,
> things that somebody doesn't like are very closely related to the things
> that they do like.
I guess it makes sense for cancellations. i guess it should become pre
Koren, Volinsky: "CF for implicit feedback datasets"
On Tue, Jun 18, 2013 at 8:07 AM, Pat Ferrel wrote:
> 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 implici
I'm suggesting using numbers like -1 for thumbs-down ratings, and then
using these as a positive weight towards 0, just like positive values
are used as positive weighting towards 1.
Most people don't make many negative ratings. For them, what you do
with these doesn't make a lot of difference. It
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
essentu
To your point Ted, I was surprised to find that remove-from-cart actions
predicted sales almost as well as purchases did but it also meant filtering
from recs. We got the best scores treating them as purchases and not
recommending them again. No one pried enough to get get bothered.
In this par
I have found that in practice, don't-like is very close to like. That is,
things that somebody doesn't like are very closely related to the things
that they do like. Things that are quite distant wind up as don't-care,
not don't-like.
This makes most simple approaches to modeling polar preferenc
Yes the model has no room for literally negative input. I think that
conceptually people do want negative input, and in this model,
negative numbers really are the natural thing to express that.
You could give negative input a small positive weight. Or extend the
definition of c so that it is mere
(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
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