Author: pat
Date: Wed Oct 1 16:56:02 2014
New Revision: 1628771
URL: http://svn.apache.org/r1628771
Log:
added an anchor
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=1628771&r1=1628770&r2=1628771&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
Wed Oct 1 16:56:02 2014
@@ -292,7 +292,7 @@ See RowSimilarityDriver.scala in Mahout'
Another use case for *spark-rowsimilarity* 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 *spark-rowsimilarity* will produce
lists of similar docs for every doc. The Apache
[Lucene](http://lucene.apache.org) project provides several methods of
[analyzing and
tokenizing](http://lucene.apache.org/core/4_9_0/core/org/apache/lucene/analysis/package-summary.html#package_description)
documents.
-#4. Creating a Unified Recommender
+#<a name="unified-recommender">4. Creating a Unified Recommender</a>
Using the output of *spark-itemsimilarity* and *spark-rowsimilarity* 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.
