Author: apalumbo
Date: Sat Mar 21 21:19:13 2015
New Revision: 1668322
URL: http://svn.apache.org/r1668322
Log:
copy recommender/quickstart to algorithms dir, rename to recommender-overview
Added:
mahout/site/mahout_cms/trunk/content/users/algorithms/recommender-overview.mdtext
Modified:
mahout/site/mahout_cms/trunk/templates/standard.html
Added:
mahout/site/mahout_cms/trunk/content/users/algorithms/recommender-overview.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/algorithms/recommender-overview.mdtext?rev=1668322&view=auto
==============================================================================
---
mahout/site/mahout_cms/trunk/content/users/algorithms/recommender-overview.mdtext
(added)
+++
mahout/site/mahout_cms/trunk/content/users/algorithms/recommender-overview.mdtext
Sat Mar 21 21:19:13 2015
@@ -0,0 +1,27 @@
+Title: Recommender Quickstart
+
+# Recommender Overview
+
+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—supplied by Mahout's new spark-itemsimilarity job, and a
"serving" component—supplied by a modern scalable search engine, like
Solr.
+
+
+
+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.
+
+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.
+
+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.
+
+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.
+
+##References
+
+1. A free ebook, which talks about the general idea: [Practical Machine
Learning](https://www.mapr.com/practical-machine-learning)
+2. A slide deck, which talks about mixing actions or other indicators:
[Creating a Multimodal Recommender with Mahout and a Search
Engine](http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/)
+3. Two blog posts: [What's New in Recommenders: part
#1](http://occamsmachete.com/ml/2014/08/11/mahout-on-spark-whats-new-in-recommenders/)
+and [What's New in Recommenders: part
#2](http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/)
+3. A post describing the loglikelihood ratio: [Surprise and
Coinsidense](http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html)
LLR is used to reduce noise in the data while keeping the calculations O(n)
complexity.
+
+##Mahout Model Creation
+
+See the page describing
[*spark-itemsimilarity*](http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html)
for more details.
\ No newline at end of file
Modified: mahout/site/mahout_cms/trunk/templates/standard.html
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/templates/standard.html?rev=1668322&r1=1668321&r2=1668322&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/templates/standard.html (original)
+++ mahout/site/mahout_cms/trunk/templates/standard.html Sat Mar 21 21:19:13
2015
@@ -168,6 +168,7 @@
<li class="nav-header">Matrix Decomposition</li>
<li><a href="/users/algorithms/d-qr.html">Distributed
QR</a></li>
<li class="nav-header">Recommendations</li>
+ <li><a
href="/users/algorithms/recommender-overview.html">Recommender Overview</a></li>
<li><a
href="/users/algorithms/intro-cooccurrence-spark.html">Intro to
cooccurrence-based<br/> recommendations with Spark</a></li>
<li class="nav-header">Classification</li>
<li><a href="/users/algorithms/spark-naive-bayes.html">Spark
Naive Bayes</a></li>
@@ -199,7 +200,6 @@
<li><a href="/users/clustering/cluster-dumper.html">Cluster
Dumper tool</a></li>
<li><a
href="/users/clustering/visualizing-sample-clusters.html">Cluster
visualisation</a></li>
<li class="nav-header">Recommendations</li>
- <li><a
href="/users/recommender/quickstart.html">Quickstart</a></li>
<li><a
href="/users/recommender/recommender-first-timer-faq.html">First Timer
FAQ</a></li>
<li><a href="/users/recommender/userbased-5-minutes.html">A
user-based recommender <br/>in 5 minutes</a></li>
<li><a
href="/users/recommender/matrix-factorization.html">Matrix
factorization-based<br/> recommenders</a></li>
