On 12/08/2011 03:19 PM, Sebastian Schelter wrote:
[...]
A model for recommenders that use matrix factorization consists of the
user and item feature vectors. You can use a FilePersistenceStrategy
with any SVDRecommender to read and write these.
In the future we could also support loading the results of
ParallelALSFactorizationJob into an SVDRecommender.
I was actually looking for this. I guess this is the one case where
there actually is a "model", and calculating the factorization can be
costly.
I would expect that doing the "SVD" offline (e.g. on Hadoop) and then
providing online recommendations which only need a simple linear
projection is a pretty common use case, isn't it? You can even take new
user preferences into account in realtime (when projecting the user
vector into the feature space) with very little cost, and just update
the transformation matrices (which should be quite static) periodically.
Bye,
Jens
