svn commit: r936845 - in /websites/staging/mahout/trunk/content: ./ users/recommender/intro-cooccurrence-spark.html

[bu...@apache.org]

Author: buildbot
Date: Mon Jan 19 22:07:31 2015
New Revision: 936845

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
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--- websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html (original)
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 be used to create "other people also liked these things" type recommendations and paired with a search engine can 
 personalize recommendations for individual users. <em>spark-rowsimilarity</em> can provide non-personalized content based 
 recommendations and when paired with a search engine can be used to personalize content based recommendations.</p>
+<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>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 Coinsidense</a>  LLR is used to reduce noise in the data while keeping the calculations O(n) complexity.</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>
 <p><em>spark-itemsimilarity</em> is the Spark counterpart of the of the Mahout mapreduce job called <em>itemsimilarity</em>. It takes in elements of interactions, which have userID, itemID, and optionally a value. It will produce one of more indicator matrices created by comparing every user's interactions with every other user. The indicator matrix is an item x item matrix where the values are log-likelihood ratio strengths. For the legacy mapreduce version, there were several possible similarity measures but these are being deprecated in favor of LLR because in practice it performs the best.</p>