Ah right. No, there's still not a provision for this. You would just have to serialize it yourself if you like. Most of the implementations don't have a great deal of startup overhead, so don't really need this. The exception is perhaps slope-one, but there you can actually save and supply pre-computed diffs. Still it would be valid to store and re-supply user-user similarities or something. You can do this, manually, by querying for user-user similarities, saving them, then loading them and supplying them via GenericUserSimilarity for instance.
On Thu, Dec 8, 2011 at 12:27 PM, Vinod <[email protected]> wrote: > Hi Sean, > > Thanks for the quick response. > > By model, I am not referring to data model but, a "trained" recommender > instance. > > Weka, for examples, has ability to save and load models:- > http://weka.wikispaces.com/Serialization > http://weka.wikispaces.com/Saving+and+loading+models > > This avoids the need to train model (recommender) every time a server is > bounced or program is restarted. > > regards, > Vinod > > > On Thu, Dec 8, 2011 at 5:43 PM, Sean Owen <[email protected]> wrote: > > > The classes aren't Serializable, no. In the case of DataModel, it's > assumed > > that you already have some persisted model somewhere, in a DB or file or > > something, so this would be redundant. > > > > On Thu, Dec 8, 2011 at 12:07 PM, Vinod <[email protected]> wrote: > > > > > Hi, > > > > > > This is my first day of experimentation with Mahout. I am following > > "Mahout > > > in Action" book and looking at the sample code provided, it seems that > > > models for ex:- recommender, needs to be trained at the start of the > > > program (start/restart). Recommender interface extends Refreshable > which > > > doesn't extend serializable. So, I am wondering if Mahout provides an > > > alternate mechanism to to persist trained models (recommender instance > in > > > this case). > > > > > > Apologies if this is a very silly question. > > > > > > Thanks & regards, > > > Vinod > > > > > >
