Github user gaborhermann commented on a diff in the pull request:
https://github.com/apache/flink/pull/2542#discussion_r80253321
--- Diff: docs/dev/libs/ml/als.md ---
@@ -49,6 +49,18 @@ By applying this step alternately to the matrices $U$
and $V$, we can iterativel
The matrix $R$ is given in its sparse representation as a tuple of $(i, j,
r)$ where $i$ denotes the row index, $j$ the column index and $r$ is the matrix
value at position $(i,j)$.
+An alternative model can be used for _implicit feedback_ datasets.
+These datasets only contain implicit feedback from the user
+in contrast to datasets with explicit feedback like movie ratings.
+For example users watch videos on a website and the website monitors which
user
+viewed which video, so the users only provide their preference implicitly.
+In these cases the feedback should not be treated as a
+rating, but rather an evidence that the user prefers that item.
+Thus, for implicit feedback datasets there is a slightly different
+minimalization problem to solve (see [Hu et
al.](http://dx.doi.org/10.1109/ICDM.2008.22) for details).
+Flink supports both explicit and implicit ALS,
+and the choice between the two can be set in the parameters.
+
--- End diff --
Okay, I added
"The implementation is based on the Apache Spark implementation of implicit
ALS."
and referred to the relevant file in the Spark codebase.
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