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 a990e0fb01c22dd96170c8511eb2995c86f21ceb..a566464d979f30a42b62e3d3b3e1b8accfa309d7 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