Thanks a bunch. That's very helpful.
On Friday, December 16, 2016, Sean Owen wrote:
> That all looks correct.
>
> On Thu, Dec 15, 2016 at 11:54 PM Manish Tripathi > wrote:
>
>> ok. Thanks. So here is what I understood.
>>
>> Input data to Als.fit(implicitPrefs=True) is the actual strengths (cou
That all looks correct.
On Thu, Dec 15, 2016 at 11:54 PM Manish Tripathi
wrote:
> ok. Thanks. So here is what I understood.
>
> Input data to Als.fit(implicitPrefs=True) is the actual strengths (count
> data). So if I have a matrix of (user,item,views/purchases) I pass that as
> the input and no
ok. Thanks. So here is what I understood.
Input data to Als.fit(implicitPrefs=True) is the actual strengths (count
data). So if I have a matrix of (user,item,views/purchases) I pass that as
the input and not the binarized one (preference). This signifies the
strength.
2) Since we also pass the al
No, input are weights or strengths. The output is a factorization of the
binarization of that to 0/1, not probs or a factorization of the input.
This explains the range of the output.
On Thu, Dec 15, 2016, 23:43 Manish Tripathi wrote:
> when you say *implicit ALS *is* factoring the 0/1 matrix. ,
when you say *implicit ALS *is* factoring the 0/1 matrix. , are you saying
for implicit feedback algorithm we need to pass the input data as the
preference matrix i.e a matrix of 0 and 1?. *
Then how will they calculate the confidence matrix which is basically
=1+alpha*count matrix. If we don't pa
No, you can't interpret the output as probabilities at all. In particular
they may be negative. It is not predicting rating but interaction. Negative
means very strongly not predicted to interact. No, implicit ALS *is*
factoring the 0/1 matrix.
On Thu, Dec 15, 2016, 23:31 Manish Tripathi wrote:
Ok. So we can kind of interpret the output as probabilities even though it
is not modeling probabilities. This is to be able to use it for
binaryclassification evaluator.
So the way I understand is and as per the algo, the predicted matrix is
basically a dot product of user factor and item factor
No, ALS is not modeling probabilities. The outputs are reconstructions of a
0/1 matrix. Most values will be in [0,1], but, it's possible to get values
outside that range.
On Thu, Dec 15, 2016 at 10:21 PM Manish Tripathi
wrote:
> Hi
>
> ran the ALS model for implicit feedback thing. Then I used t
