Currently this is not supported. If you want to do incremental fold-in of new data you would need to do it outside of Spark (e.g. see this discussion: https://mail-archives.apache.org/mod_mbox/spark-user/201603.mbox/browser, which also mentions a streaming on-line MF implementation with SGD).
In general, for serving situations MF models are stored in some other serving system, so that system may be better suited to do the actual fold-in. Sean's Oryx project does that, though I'm not sure offhand if that part is done in Spark or not. I know Sean's old Myrrix project also used to support computing ALS with an initial set of input factors, so you could in theory incrementally compute on new data. I'm not sure if the newer Oryx project supports it though. @Sean, what are your thoughts on supporting an initial model (factors) in ALS? I personally have always just recomputed the model, but for very large scale stuff it can make a lot of sense obviously. What I'm not sure on is whether it gives good solutions (relative to recomputing) - I'd imagine it will tend to find a slightly better local minimum given a previous local minimum starting point... with the advantage that new users / items are incorporated. But of course users can do a full recompute periodically. On Fri, 11 Mar 2016 at 13:04 Roberto Pagliari <roberto.pagli...@asos.com> wrote: > In the current implementation of ALS with implicit feedback, when new date > come in, it is not possible to update user/product matrices without > re-computing everything. > > Is this feature in planning or any known work around? > > Thank you, > >
