Currently, no to both questions. Models are immutable, just like the basic data structures (SFrame and SGraph). So to update a model means creating a new model. From a machine learning perspective, warm start may or may not help with faster convergence on the new dataset. (At least I've seen both behaviors, though not specifically for matrix factorization.)
But we can provide syntactic API sugar to make the call easier. Are you looking for custom initialization mainly as a way to get fast model updates? Or for something else? Would something like the following work for you?
newmodel = oldmodel.update(newobservationdata, ...)