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From p..@apache.org
Subject svn commit: r1653133 - /mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
Date Mon, 19 Jan 2015 22:07:26 GMT
Author: pat
Date: Mon Jan 19 22:07:26 2015
New Revision: 1653133

URL: http://svn.apache.org/r1653133
Log:
added links to references

Modified:
    mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext

Modified: mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
URL: http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext?rev=1653133&r1=1653132&r2=1653133&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
(original)
+++ mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
Mon Jan 19 22:07:26 2015
@@ -5,6 +5,14 @@ be used to create "other people also lik
 personalize recommendations for individual users. *spark-rowsimilarity* can provide non-personalized
content based 
 recommendations and when paired with a search engine can be used to personalize content based
recommendations.
 
+##References
+
+1. A free ebook, which talks about the general idea: [Practical Machine Learning](https://www.mapr.com/practical-machine-learning)
+2. A slide deck, which talks about mixing actions or other indicators: [Creating a Unified
Recommender](http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/)
+3. Two blog posts: [What's New in Recommenders: part #1](http://occamsmachete.com/ml/2014/08/11/mahout-on-spark-whats-new-in-recommenders/)
+and  [What's New in Recommenders: part #2](http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/)
+3. A post describing the loglikelihood ratio:  [Surprise and Coinsidense](http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html)
 LLR is used to reduce noise in the data while keeping the calculations O(n) complexity.
+
 Below are the command line jobs but the drivers and associated code can also be customized
and accessed from the Scala APIs.
 
 ##1. spark-itemsimilarity



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