svn commit: r942924 - in /websites/staging/mahout/trunk/content: ./ users/recommender/quickstart.html

[buildbot]

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>