One other idea was that I don’t need to re-train the model, but simply pass all the current user’s recent ratings (including one’s created after the training) to the existing model…
Is this a valid option? -------- Wouter Samaey Zaakvoerder Storefront BVBA Tel: +32 472 72 83 07 Web: http://storefront.be LinkedIn: http://www.linkedin.com/in/woutersamaey > On 05 Jan 2015, at 13:13, Sean Owen <so...@cloudera.com> wrote: > > In the first instance, I'm suggesting that ALS in Spark could perhaps > expose a run() method that accepts a previous > MatrixFactorizationModel, and uses the product factors from it as the > initial state instead. If anybody seconds that idea, I'll make a PR. > > The second idea is just fold-in: > http://www.slideshare.net/srowen/big-practical-recommendations-with-alternating-least-squares/14 > > Whether you do this or something like SGD, inside or outside Spark, > depends on your requirements I think. > > On Sat, Jan 3, 2015 at 12:04 PM, Wouter Samaey > <wouter.sam...@storefront.be> wrote: >> Do you know a place where I could find a sample or tutorial for this? >> >> I'm still very new at this. And struggling a bit... >> >> Thanks in advance >> >> Wouter >> >> Sent from my iPhone. >> >> On 03 Jan 2015, at 10:36, Sean Owen <so...@cloudera.com> wrote: >> >> Yes, it is easy to simply start a new factorization from the current model >> solution. It works well. That's more like incremental *batch* rebuilding of >> the model. That is not in MLlib but fairly trivial to add. >> >> You can certainly 'fold in' new data to approximately update with one new >> datum too, which you can find online. This is not quite the same idea as >> streaming SGD. I'm not sure this fits the RDD model well since it entails >> updating one element at a time but mini batch could be reasonable. >> >> On Jan 3, 2015 5:29 AM, "Peng Cheng" <rhw...@gmail.com> wrote: >>> >>> 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 >>>> >>> >> --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
