There is a general movement to allowing initial models to be specified for
Spark ML algorithms, so I'll add a JIRA to that task set. I should be able
to work on this as well as other ALS improvements.
Oh, another reason fold-in is typically not done in Spark is that for
models of any reasonable si
On Fri, Mar 11, 2016 at 12:18 PM, Nick Pentreath
wrote:
> 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 Spa
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 g
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,
