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| author | Nick Pentreath <nickp@za.ibm.com> | 2016-05-24 11:34:06 -0700 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2016-05-24 11:34:06 -0700 |
| commit | 20900e5feced76e87f0a12823d0e3f07e082105f (patch) | |
| tree | b1f571705667628a1788b1d87a829d2b6dc870a5 /docs/ml-collaborative-filtering.md | |
| parent | be99a99fe7976419d727c0cc92e872aa4af58bf1 (diff) | |
| download | spark-20900e5feced76e87f0a12823d0e3f07e082105f.tar.gz spark-20900e5feced76e87f0a12823d0e3f07e082105f.tar.bz2 spark-20900e5feced76e87f0a12823d0e3f07e082105f.zip | |
[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.
Diffstat (limited to 'docs/ml-collaborative-filtering.md')
| -rw-r--r-- | docs/ml-collaborative-filtering.md | 6 |
1 files changed, 5 insertions, 1 deletions
diff --git a/docs/ml-collaborative-filtering.md b/docs/ml-collaborative-filtering.md index bd3d527d9a..8bd75f3bcf 100644 --- a/docs/ml-collaborative-filtering.md +++ b/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, |