I was under the impression that ALS wasn't designed for it :-< The famous ebay online recommender uses SGD However, you can try using the previous model as starting point, and gradually reduce the number of iteration after the model stablize. I never verify this idea, so you need to at least cross-validate it before putting into productio
On 2 January 2015 at 04:40, Wouter Samaey <wouter.sam...@storefront.be> wrote: > Hi all, > > I'm curious about MLlib and if it is possible to do incremental training on > the ALSModel. > > Usually training is run first, and then you can query. But in my case, data > is collected in real-time and I want the predictions of my ALSModel to > consider the latest data without complete re-training phase. > > I've checked out these resources, but could not find any info on how to > solve this: > https://spark.apache.org/docs/latest/mllib-collaborative-filtering.html > > http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html > > My question fits in a larger picture where I'm using Prediction IO, and > this > in turn is based on Spark. > > Thanks in advance for any advice! > > Wouter > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Is-it-possible-to-do-incremental-training-using-ALSModel-MLlib-tp20942.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >
