Author: buildbot
Date: Sat Feb 7 16:33:43 2015
New Revision: 939356
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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-1653133
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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
Sat Feb 7 16:33:43 2015
@@ -246,9 +246,14 @@
<div id="content-wrap" class="clearfix">
<div id="main">
<h1 id="intro-to-cooccurrence-recommenders-with-spark">Intro to
Cooccurrence Recommenders with Spark</h1>
+<p>Mahout's next generation recommender is based on the proven cooccurrence
algorithm but takes it several important steps further
+by creating a multimodal recommender, which can make use of many user actions
to make recommendations. In the old days
+only page reads, or purchases could be used alone. Now search terms,
locations, all manner of clickstream data can be used to
+recommend - hence the term multimodal. It also allows the recommendations to
be tuned for the placement context by changine
+the query without recalculating the model - adding to its multimodality.</p>
<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
+personalize multimodal recommendations for individual users.
<em>spark-rowsimilarity</em> can provide non-personalized content based
recommendations and when paired with a search engine can be used to
personalize content based recommendations.</p>
<h2 id="references">References</h2>
<ol>
@@ -256,7 +261,7 @@ recommendations and when paired with a s
<li>A slide deck, which talks about mixing actions or other indicators: <a
href="http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/";>Creating
a Unified Recommender</a></li>
<li>Two blog posts: <a
href="http://occamsmachete.com/ml/2014/08/11/mahout-on-spark-whats-new-in-recommenders/";>What's
New in Recommenders: part #1</a>
and <a
href="http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/";>What's
New in Recommenders: part #2</a></li>
-<li>A post describing the loglikelihood ratio: <a
href="http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html";>Surprise
and Coinsidense</a> LLR is used to reduce noise in the data while keeping the
calculations O(n) complexity.</li>
+<li>A post describing the loglikelihood ratio: <a
href="http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html";>Surprise
and Coinsidence</a> LLR is used to reduce noise in the data while keeping the
calculations O(n) complexity.</li>
</ol>
<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>
