Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/422#discussion_r11842061
--- 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).
--- End diff --
number of blacks -> number of blocks
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