Hi all - I’m using pySpark/MLLib ALS for user/item clustering and would like to directly access the user/product RDDs (called userFeatures/productFeatures in class MatrixFactorizationModel in mllib/recommendation/MatrixFactorizationModel.scala
This doesn’t seem to complex, but it doesn’t seem like the functionality is currently available. I think it requires accessing the underlying java mode like so: model = ALS.train(ratings,1,iterations=1,blocks=5) userFeatures = RDD(model.javamodel.userFeatures, sc, ???) However, I don’t know what to pass as the deserializer. I need these low dimensional vectors as an RDD to then use in Kmeans clustering. Has anyone done something similar? Ben ________________________________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.
