For a low-rank matrix factorization based recommender, a new preference
is not itself, but a dot product of two vectors in the low dimensional
space, so it needs no projection. The user and item vectors however may
need to be projected into a lower dimensional space, if and only if you
want to reduce the rank of the preference matrix. The refactorization
step in SGD is super fast--that's the charm of SGD. So, yes, we will
refactorize in every update.
Yours Peng
On 13-07-18 11:34 AM, Pat Ferrel wrote:
On Jul 17, 2013, at 1:19 PM, Gokhan Capan <[email protected]> wrote:
Hi Pat, please see my response inline.
Best,
Gokhan
On Wed, Jul 17, 2013 at 8:23 PM, Pat Ferrel <[email protected]> wrote:
May I ask how you plan to support model updates and 'anonymous' users?
I assume the latent factors model is calculated offline still in batch
mode, then there are periodic updates? How are the updates handled?
If you are referring to the recommender of discussion here, no, updating
the model can be done with a single preference, using stochastic gradient
descent, by updating the particular user and item factors simultaneously.
Aren't there two different things needed to truly update the model: 1) add the
new preference to the lower dimensional space 2) refactorize the all
preferences. #2 only needs to be done periodically--afaik. #1 would be super
fast and could be done at runtime. Am I wrong or are you planning to
incrementally refactorize the entire preference array with every new preference?
