Author: buildbot
Date: Wed Oct 1 21:53:30 2014
New Revision: 924330
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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--- cms:source-revision (original)
+++ cms:source-revision Wed Oct 1 21:53:30 2014
@@ -1 +1 @@
-1628782
+1628847
Modified:
websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
==============================================================================
---
websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
(original)
+++
websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
Wed Oct 1 21:53:30 2014
@@ -249,7 +249,7 @@
<p>Mahout provides several important building blocks for creating
recommendations using Spark. <em>spark-itemsimilarity</em> can
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, using textual content for example.</p>
+recommendations and when paired with a search engine can be used to
personalize content based recommendations.</p>
<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>
