diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 00fe741383db1..5a01dfcd2baee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -107,7 +107,7 @@ class MatrixFactorizationModel private[mllib] (
     scored.top(num)(Ordering.by(_._2))
   }
 
-  def userFeaturesString(userFeatures: RDD[(Int, Array[Double])]): RDD[String]  = {
+  def userFeaturesString(): RDD[String]  = {
     userFeatures.map(element => element._1+","+element._2.mkString(","))
   }
 }
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 59c1c5ff0ced0..2ab4e71534200 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -82,6 +82,11 @@ def predictAll(self, user_product):
         return RDD(sc._jvm.PythonRDD.javaToPython(jresult), sc,
                    AutoBatchedSerializer(PickleSerializer()))
 
+    def userFeatures(self):
+        sc = self._context
+        juf = self._java_model.userFeaturesString().toJavaRDD()
+        return RDD(sc._jvm.PythonRDD.javaToPython(juf), sc,
+                   AutoBatchedSerializer(PickleSerializer()))
 
 class ALS(object):