[SPARK-15502][DOC][ML][PYSPARK] add guide note that ALS only supports integer ids

This PR adds a note to clarify that the ML API for ALS only supports integers for user/item ids, and that other types for these columns can be used but the ids must fall within integer range.

(Refer [SPARK-14891](https://issues.apache.org/jira/browse/SPARK-14891)).

Also cleaned up a reference to `mllib` in the ML doc.

## How was this patch tested?
Built and viewed User Guide doc locally.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #13278 from MLnick/SPARK-15502-als-int-id-doc-note.

docs/ml-collaborative-filtering.md

@@ -29,6 +29,10 @@ following parameters:
   *baseline* confidence in preference observations (defaults to 1.0).
 * *nonnegative* specifies whether or not to use nonnegative constraints for least squares (defaults to `false`).
 
+**Note:** The DataFrame-based API for ALS currently only supports integers for user and item ids.
+Other numeric types are supported for the user and item id columns, 
+but the ids must be within the integer value range. 
+
 ### Explicit vs. implicit feedback
 
 The standard approach to matrix factorization based collaborative filtering treats 
@@ -36,7 +40,7 @@ the entries in the user-item matrix as *explicit* preferences given by the user
 for example, users giving ratings to movies.
 
 It is common in many real-world use cases to only have access to *implicit feedback* (e.g. views,
-clicks, purchases, likes, shares etc.). The approach used in `spark.mllib` to deal with such data is taken
+clicks, purchases, likes, shares etc.). The approach used in `spark.ml` to deal with such data is taken
 from [Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22).
 Essentially, instead of trying to model the matrix of ratings directly, this approach treats the data
 as numbers representing the *strength* in observations of user actions (such as the number of clicks,