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From build...@apache.org
Subject svn commit: r942924 - in /websites/staging/mahout/trunk/content: ./ users/recommender/quickstart.html
Date Sun, 08 Mar 2015 22:30:00 GMT
Author: buildbot
Date: Sun Mar  8 22:30:00 2015
New Revision: 942924

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/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Sun Mar  8 22:30:00 2015
@@ -1 +1 @@
-1665077
+1665091

Modified: websites/staging/mahout/trunk/content/users/recommender/quickstart.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/recommender/quickstart.html (original)
+++ websites/staging/mahout/trunk/content/users/recommender/quickstart.html Sun Mar  8 22:30:00
2015
@@ -248,10 +248,10 @@
     <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://i.imgur.com/fliHMBo.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>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 to log user 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>
-<p>All ids for users and items are as preserved as string tokens and so work as an
external key in DBs or as doc ids for search engines, they also work as tokens for search
queries.</p>
+<p>All ids for users and items are preserved as string tokens and so work as an external
key in DBs or as doc ids for search engines, they also work as tokens for search queries.</p>
 <h2 id="references">References</h2>
 <ol>
 <li>A free ebook, which talks about the general idea: <a href="https://www.mapr.com/practical-machine-learning">Practical
Machine Learning</a></li>



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