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From Mikael Ståldal <>
Subject Load whole ALS MatrixFactorizationModel into memory
Date Wed, 02 Nov 2016 16:53:57 GMT
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel

I build a MatrixFactorizationModel with ALS.trainImplicit(), then I save it
with its save method.

Later, in an other process on another machine, I load the model with
MatrixFactorizationModel.load(). Now I want to make a lot of
recommendProducts() calls on the loaded model, and I want them to be quick,
without any I/O. However, they are slow and seem to to I/O each time.

Is there any way to force loading the whole model into memory (that step
can take some time and do I/O) and then be able to do recommendProducts()
on it multiple times, quickly without I/O?

[image: MagineTV]

*Mikael Ståldal*
Senior software developer

*Magine TV*
Grev Turegatan 3  | 114 46 Stockholm, Sweden  |

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