In ALS the coincidence matrix is approximated by XY',
where X is user-feature, Y is item-feature.
Now, here is the question:
are/should the feature vectors be normalized before computing
recommendations?
Now, what happens in the case of SVD?
The vectors are normal by definition.
Are singular
On Wed, Sep 4, 2013 at 10:07 AM, Koobas koo...@gmail.com wrote:
In ALS the coincidence matrix is approximated by XY',
where X is user-feature, Y is item-feature.
Now, here is the question:
are/should the feature vectors be normalized before computing
recommendations?
if it is a coincidence
Let me rephrase.
Suppose I did ALS decomposition of a matrix.
Suppose I don't want to produce recommendations
(by calculating XY').
Suppose I want to find users with similar preferences
(by calculating XX').
Should the correlation of a user with himself be 1.0?
If the answer is yes, that
On Wed, Sep 4, 2013 at 10:59 AM, Dmitriy Lyubimov dlie...@gmail.com wrote:
Now, what happens in the case of SVD?
The vectors are normal by definition.
Are singular values used at all, or just left and right singular vectors?
SVD does not take weights so it cannot ignore or weigh out a
On Wed, Sep 4, 2013 at 11:33 AM, Koobas koo...@gmail.com wrote:
Let me rephrase.
Suppose I did ALS decomposition of a matrix.
Suppose I don't want to produce recommendations
(by calculating XY').
Suppose I want to find users with similar preferences
(by calculating XX').
Should the
On Wed, Sep 4, 2013 at 11:43 AM, Ted Dunning ted.dunn...@gmail.com wrote:
On Wed, Sep 4, 2013 at 10:59 AM, Dmitriy Lyubimov dlie...@gmail.com wrote:
Now, what happens in the case of SVD?
The vectors are normal by definition.
Are singular values used at all, or just left and right singular
The feature vectors? rows of X and Y? no, they definitely should not be
normalized. It will change the approximation you so carefully built quite a
lot.
As you say U and V are orthornormal in the SVD. But you still multiply all
of them together with Sigma when making recs. (Or you embed Sigma in
On Wed, Sep 4, 2013 at 3:06 PM, Sean Owen sro...@gmail.com wrote:
The feature vectors? rows of X and Y? no, they definitely should not be
normalized. It will change the approximation you so carefully built quite a
lot.
As you say U and V are orthornormal in the SVD. But you still multiply
