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-rw-r--r--mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala2
1 files changed, 1 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index a990e0fb01..a566464d97 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -81,7 +81,7 @@ case class Rating(val user: Int, val product: Int, val rating: Double)
*
* For implicit preference data, the algorithm used is based on
* "Collaborative Filtering for Implicit Feedback Datasets", available at
- * [[http://research.yahoo.com/pub/2433]], adapted for the blocked approach used here.
+ * [[http://dx.doi.org/10.1109/ICDM.2008.22]], adapted for the blocked approach used here.
*
* Essentially instead of finding the low-rank approximations to the rating matrix `R`,
* this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if