Hi there, I'm wondering if it's possible (or feasible) to combine the feature matrices of two MatrixFactorizationModels that share a user and product set.
Specifically, one model would be the "on-going" model, and the other is one trained only on the most recent aggregation of some event data. My overall goal is to try to approximate "online" training, as ALS doesn't support streaming, and it also isn't possible to "seed" the ALS training process with an already trained model. Since the two Models would share a user/product ID space, can their feature matrices be merged? For instance via: 1. Adding feature vectors together for user/product vectors that appear in both models 2. Averaging said vectors instead 3. Some other linear algebra operation Unfortunately, I'm fairly ignorant as to the internal mechanics of ALS itself. Is what I'm asking possible? Thank you, Colin
