Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/10411#discussion_r52830580
--- Diff: docs/mllib-collaborative-filtering.md ---
@@ -32,16 +32,16 @@ following parameters:
The standard approach to matrix factorization based collaborative
filtering treats
the entries in the user-item matrix as *explicit* preferences given by the
user to the item.
+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
-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 a combination of binary preferences and *confidence values*. The
ratings are then related to the
-level of confidence in observed user preferences, rather than explicit
ratings given to items. The
-model then tries to find latent factors that can be used to predict the
expected preference of a
-user for an item.
+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
--- End diff --
@srowen tried to take your remarks into account, I don't know if it's
clearer now though.
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