Author: buildbot
Date: Sat Feb 14 17:14:41 2015
New Revision: 940163
Log:
Staging update by buildbot for mahout
Modified:
websites/staging/mahout/trunk/content/ (props changed)
websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
Propchange: websites/staging/mahout/trunk/content/
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Modified:
websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
==============================================================================
---
websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
(original)
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websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
Sat Feb 14 17:14:41 2015
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<h2 id="references">References</h2>
<ol>
<li>A free ebook, which talks about the general idea: <a
href="https://www.mapr.com/practical-machine-learning";>Practical Machine
Learning</a></li>
-<li>A slide deck, which talks about mixing actions or other indicators: <a
href="http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/";>Creating
a Unified Recommender</a></li>
+<li>A slide deck, which talks about mixing user actions and other indicators:
<a
href="http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/";>Multimodal
Streaming Recommender</a></li>
<li>Two blog posts: <a
href="http://occamsmachete.com/ml/2014/08/11/mahout-on-spark-whats-new-in-recommenders/";>What's
New in Recommenders: part #1</a>
and <a
href="http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/";>What's
New in Recommenders: part #2</a></li>
-<li>A post describing the loglikelihood ratio: <a
href="http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html";>Surprise
and Coinsidence</a> LLR is used to reduce noise in the data while keeping the
calculations O(n) complexity.</li>
+<li>A post describing the loglikelihood ratio: <a
href="http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html";>Surprise
and Coinsidense</a> LLR is used to reduce noise in the data while keeping the
calculations O(n) complexity.</li>
+<li>A demo <a href="https://guide.finderbots.com";>Video Guide</a> site, which
uses many of the techniques described above.</li>
</ol>
<p>Below are the command line jobs but the drivers and associated code can
also be customized and accessed from the Scala APIs.</p>
<h2 id="1-spark-itemsimilarity">1. spark-itemsimilarity</h2>
