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From build...@apache.org
Subject svn commit: r900682 - in /websites/staging/mahout/trunk/content: ./ users/recommender/quickstart.html
Date Sun, 09 Mar 2014 08:55:41 GMT
Author: buildbot
Date: Sun Mar  9 08:55:41 2014
New Revision: 900682

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/recommender/quickstart.html

Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Sun Mar  9 08:55:41 2014
@@ -1 +1 @@
-1575678
+1575681

Modified: websites/staging/mahout/trunk/content/users/recommender/quickstart.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/recommender/quickstart.html (original)
+++ websites/staging/mahout/trunk/content/users/recommender/quickstart.html Sun Mar  9 08:55:41
2014
@@ -221,8 +221,15 @@
   <div id="content-wrap" class="clearfix">
    <div id="main">
     <h1 id="recommender-quickstart">Recommender Quickstart</h1>
-<p>It's very easy to get started with Mahout's recommenders. You don't need to know
and have Hadoop for this.</p>
+<p>It's very easy to get started with Mahout's recommenders. You don't need to know
and have Hadoop for this. Here we list resources that might be helpful for some first steps:</p>
+<ul>
+<li>
 <p>Steve Cook created a <a href="https://www.youtube.com/watch?v=yD40rVKUwPI">video
tutorial</a> on how to create a simple item-based recommender from scratch using Eclipse.
(Note that you can avoid manually downloading the library jars by including mahout as <a
href="/general/downloads.html">maven dependency</a> into your project). </p>
+</li>
+<li>
+<p>The paper <a href="http://ssc.io/wp-content/uploads/2013/02/cf-mahout.pdf">Collaborative
Filtering with Apache Mahout</a> by Sebastian Schelter and Sean Owen gives a short overview
of Mahout's non-distributed recommenders and has pointers to research papers describing the
underlying algorithms. </p>
+</li>
+</ul>
    </div>
   </div>     
 </div> 



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