I trained MatrixFactorizationModelwith help ALS.train()and now using model.recommendProducts(user, num)to get the best recommended products, but the code does not work for some users with the following error:
user_products = model.call("recommendProducts", user, prodNum)
File "/usr/lib/spark/python/pyspark/mllib/common.py", line 136, in call
return callJavaFunc(self._sc, getattr(self._java_model, name), *a)
File "/usr/lib/spark/python/pyspark/mllib/common.py", line 113, in callJavaFunc
return _java2py(sc, func(*args))
File "/usr/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
File "/usr/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o68.recommendProducts.
: java.util.NoSuchElementException: next on empty iterator
at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:64)
at scala.collection.IterableLike$class.head(IterableLike.scala:91)
at scala.collection.mutable.WrappedArray.scala$collection$IndexedSeqOptimized$$super$head(WrappedArray.scala:34)
at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:120)
at scala.collection.mutable.WrappedArray.head(WrappedArray.scala:34)
at org.apache.spark.mllib.recommendation.MatrixFactorizationModel.recommendProducts(MatrixFactorizationModel.scala:117)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
As you can see in the first line above, I run
user_products = model.call("recommendProducts", user, prodNum)
instead
user_products = model.recommendProducts(user, prodNum)
because the latter is not implemented in 1.3.0 pyspark, which I use. In any case, it correctly returns the prediction for some users, but then it fails on others.
I understand that he probably does not have the exact number of predictions that I request, I would expect that he will return less.