svn commit: r942886 - in /websites/staging/mahout/trunk/content: ./ users/recommender/quickstart.html

[bu...@apache.org]

Author: buildbot
Date: Sun Mar  8 17:10:33 2015
New Revision: 942886

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/recommender/quickstart.html

Propchange: websites/staging/mahout/trunk/content/
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--- cms:source-revision (original)
+++ cms:source-revision Sun Mar  8 17:10:33 2015
@@ -1 +1 @@
-1665051
+1665055

Modified: websites/staging/mahout/trunk/content/users/recommender/quickstart.html
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--- websites/staging/mahout/trunk/content/users/recommender/quickstart.html (original)
+++ websites/staging/mahout/trunk/content/users/recommender/quickstart.html Sun Mar  8 17:10:33 2015
@@ -247,7 +247,7 @@
    <div id="main">
     <h1 id="recommender-overview">Recommender Overview</h1>
 <p>Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. But to get the best  out of our more modern aproach we'll need to think of the Recommender as a "model creation" component&mdash;supplied by Mahout's new spark-itemsimilarity job, and a "serving" component&mdash;supplied by a modern scalable search engine, like Solr.</p>
-<p><img alt="image" src="http://postimg.org/image/6yw9b3fdn/" /></p>
+<p><img alt="image" src="http://s6.postimg.org/r0m8bpjw1/recommender_architecture.png" /></p>
 <p>To integrate with your application you will collect user interactions storing them in a DB and also in a from usable by Mahout. The simplest way to do this is log interactions to csv files (user-id, item-id). The DB should be setup to contain the last n user interactions, which will form part of the query for recommendations.</p>
 <p>Mahout's spark-itemsimilarity will create a table of (item-id, list-of-similar-items) in csv form. Think of this as an item collection with one field containing the item-ids of similar items. Index this with your search engine. </p>
 <p>When your application needs recommendations for a specific person, get the latest user history of interactions from the DB and query the indicator collection with this history. You will get back an ordered list of item-ids. These are your recommendations. You may wish to filter out any that the user has already seen but that will depend on your use case.</p>