There is no inherent mathematical difference, but there may be some pretty
significant practical differences.
Using the three matrix form (X = USV') puts the normalization constants
into a place where you can control them a bit easier. This can be useful
if you want *both* user and item vectors that are normalized.
If you only want item vectors, then it really doesn't matter since you can
incorporate as much of S as you like into the item vectors as you like and
the rest winds up in the factor that you aren't looking at anyway.
On Thu, Nov 20, 2014 at 1:34 AM, Parimi Rohit rohit.par...@gmail.com
wrote:
Hi All,
Are there any (dis)advantages of using tri-factorization (||X - USV'||) as
opposed to bi-factorization ((||X - UV'||)) for recommender systems? I have
been reading a lot about tri-factorization and how they can be seen as
co-clustering of rows and columns and was wondering if such as technique is
implemented in Mahout?
Also, I am particularly interested in implicit-feedback datasets and the
only MF approach I am aware of is the ALS-WR for implicit feedback data
implemented in mahout. Are there any other MF techniques? If not, is it
possible (and useful) to extend some tri-factorization to handle
implicit-feedback along the lines of Collaborative Filtering for Implicit
Feedback Datasets (the approach implemented in Mahout).
I apologize for any inconvenience as this question is very general and
might not be relevant to Mahout and I would really appreciate any
thoughts/feedback.
Thanks,
Rohit
