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
>>> 
>>> 
>>> 
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