svn commit: r924308 - in /websites/staging/mahout/trunk/content: ./ users/recommender/intro-cooccurrence-spark.html

[bu...@apache.org]

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>