git commit: [SPARK-1273] use doi links in mllib-guide

[td...@apache.org]

Repository: spark
Updated Branches:
  refs/heads/branch-0.9 8b1e79323 -> d68549e8f


[SPARK-1273] use doi links in mllib-guide

Author: Xiangrui Meng <me...@databricks.com>

Closes #198 from mengxr/branch-0.9 and squashes the following commits:

39c74ff [Xiangrui Meng] use doi links in mllib-guide


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/d68549e8
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/d68549e8
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/d68549e8

Branch: refs/heads/branch-0.9
Commit: d68549e8f84f61eadc6907be306fd4fc7a95692e
Parents: 8b1e793
Author: Xiangrui Meng <me...@databricks.com>
Authored: Fri Mar 21 14:35:32 2014 -0700
Committer: Tathagata Das <ta...@gmail.com>
Committed: Fri Mar 21 14:35:32 2014 -0700

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 docs/mllib-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/d68549e8/docs/mllib-guide.md
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diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index a22a221..2e3e817 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -105,7 +105,7 @@ 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://dx.doi.org/10.1109/MC.2009.263)
 algorithm to learn these latent factors. The implementation in MLlib has the
 following parameters:
 
@@ -124,7 +124,7 @@ the entries in the user-item matrix as *explicit* preferences given by the user
 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 MLlib to deal with 
 such data is taken from 
-[Collaborative Filtering for Implicit Feedback Datasets](http://www2.research.att.com/~yifanhu/PUB/cf.pdf).
+[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.