It looks like you mixed use ALS in spark.ml and spark.mllib package.
You can train the model by either one, meanwhile, you should use the
corresponding save/load functions.
You can not train/save the model by spark.mllib ALS, and then use spark.ml
ALS to load the model. It will throw exceptions.
Hello guys: I have a problem in loading recommend model. I have 2 models,
one is good(able to get recommend result) and another is not working. I checked
these 2 models, both are MatrixFactorizationModel object. But in the metadata,
one is a PipelineModel and another is a
