I have used thumbs-down-like interactions as like an anti-click, and
subtracts from the interaction between the user and item. The negative
scores can be naturally applied in a matrix-factorization-like model
like ALS, but that's not the situation here.
Others probably have better first-hand
I like the negative click analogy. The data shows an explicit interaction—using
only thumbs up ignores that interaction. Yes, the cooccurrence style
recommender can’t account for these in the same way ALS does but filtering them
seems like a close approximation and maybe good enough.
#1 asks
It is bad practice to use weightings to express different actions. This
may be necessary in an ALS framework, but it is still a bad idea.
A much better approach is to use multi-modal recommendation in which each
action is used independently in a cross-recommendation fashion to measure
predictive
On Aug 15, 2014, at 9:05 AM, Ted Dunning ted.dunn...@gmail.com wrote:
It is bad practice to use weightings to express different actions. This
may be necessary in an ALS framework, but it is still a bad idea.
A much better approach is to use multi-modal recommendation in which each
On Fri, Aug 15, 2014 at 2:24 PM, Pat Ferrel pat.fer...@gmail.com wrote:
On Aug 15, 2014, at 9:05 AM, Ted Dunning ted.dunn...@gmail.com wrote:
It is bad practice to use weightings to express different actions. This
may be necessary in an ALS framework, but it is still a bad idea.
A
Now that we have multi-action/cross-cooccurrences in ItemSimilarity we can
start playing with taking in multiple actions to recommend one. On the demo
site I have data for thumbs up and down but have only been using thumbs up as
the primary action. I then filter recs by a user’s thumbs down
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
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
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
They are on a lot of papers, which are you looking at?
On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov dlie...@gmail.com 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
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.
Koren, Volinsky: CF for implicit feedback datasets
On Tue, Jun 18, 2013 at 8:07 AM, Pat Ferrel p...@occamsmachete.com wrote:
They are on a lot of papers, which are you looking at?
On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov dlie...@gmail.com wrote:
(Kinda doing something very close. )
On Tue, Jun 18, 2013 at 3:48 AM, Ted Dunning ted.dunn...@gmail.com 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
(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
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