Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/422#discussion_r11842077
--- Diff: docs/mllib-collaborative-filtering.md ---
@@ -14,44 +14,43 @@ missing entries of a user-item association matrix.
MLlib currently supports
model-based collaborative filtering, in which users and products are
described
by a small set of latent factors that can be used to predict missing
entries.
In particular, we implement the [alternating least squares
-(ALS)](http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf)
+(ALS)](http://dl.acm.org/citation.cfm?id=1608614)
algorithm to learn these latent factors. The implementation in MLlib has
the
following parameters:
-* *numBlocks* is the number of blacks used to parallelize computation (set
to -1 to auto-configure).
+* *numBlocks* is the number of blacks used to parallelize computation (set
to -1 to auto-configure).
* *rank* is the number of latent factors in our model.
* *iterations* is the number of iterations to run.
* *lambda* specifies the regularization parameter in ALS.
-* *implicitPrefs* specifies whether to use the *explicit feedback* ALS
variant or one adapted for *implicit feedback* data
-* *alpha* is a parameter applicable to the implicit feedback variant of
ALS that governs the *baseline* confidence in preference observations
+* *implicitPrefs* specifies whether to use the *explicit feedback* ALS
variant or one adapted for
--- End diff --
These last two points lack periods, whereas every other point has periods.
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