Author: buildbot
Date: Wed Oct 1 16:56:06 2014
New Revision: 924308
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 16:56:06 2014
@@ -1 +1 @@
-1628770
+1628771
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 16:56:06 2014
@@ -526,7 +526,7 @@ by a list of the most similar rows.</p>
<p>See RowSimilarityDriver.scala in Mahout's spark module if you want to
customize the code. </p>
<h1 id="3-using-spark-rowsimilarity-with-text-data">3. Using
<em>spark-rowsimilarity</em> with Text Data</h1>
<p>Another use case for <em>spark-rowsimilarity</em> is in finding similar
textual content. For instance given the content of a blog post, which other
posts are similar. In this case the columns are terms and the rows are
documents. Since LLR is the only similarity method supported this is not the
optimal way to determine document similarity. LLR is used more as a quality of
similarity filter than as a similarity measure. However
<em>spark-rowsimilarity</em> will produce lists of similar docs for every doc.
The Apache <a href="http://lucene.apache.org";>Lucene</a> project provides
several methods of <a
href="http://lucene.apache.org/core/4_9_0/core/org/apache/lucene/analysis/package-summary.html#package_description";>analyzing
and tokenizing</a> documents.</p>
-<h1 id="4-creating-a-unified-recommender">4. Creating a Unified
Recommender</h1>
+<h1 id="wzxhzdk234-creating-a-unified-recommenderwzxhzdk24"><a
name="unified-recommender">4. Creating a Unified Recommender</a></h1>
<p>Using the output of <em>spark-itemsimilarity</em> and
<em>spark-rowsimilarity</em> you can build a unified cooccurrnce and content
based recommender that can be used in both or either mode depending on
indicators available and the history available at runtime for a user.</p>
<h2 id="requirements">Requirements</h2>
<ol>
