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* mikael.stal...@magine.com Grev Turegatan 3 | 114 46 Stockholm, Sweden | www.magine.com Privileged and/or Confidential Information may be contained in this message. If you are not the addressee indicated in this message (or responsible for delivery of the message to such a person), you may not copy or deliver this message to anyone. In such case, you should destroy this message and kindly notify the sender by reply email.
