Author: pat
Date: Sat Feb 7 16:33:39 2015
New Revision: 1658070
URL: http://svn.apache.org/r1658070
Log:
Description of the term multimodal
Modified:
mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
Modified:
mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext?rev=1658070&r1=1658069&r2=1658070&view=diff
==============================================================================
---
mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
(original)
+++
mahout/site/mahout_cms/trunk/content/users/recommender/intro-cooccurrence-spark.mdtext
Sat Feb 7 16:33:39 2015
@@ -1,8 +1,14 @@
#Intro to Cooccurrence Recommenders with Spark
+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.
+
Mahout provides several important building blocks for creating recommendations
using Spark. *spark-itemsimilarity* 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. *spark-rowsimilarity* can
provide non-personalized content based
+personalize multimodal recommendations for individual users.
*spark-rowsimilarity* can provide non-personalized content based
recommendations and when paired with a search engine can be used to
personalize content based recommendations.
##References
@@ -11,7 +17,7 @@ recommendations and when paired with a s
2. A slide deck, which talks about mixing actions or other indicators:
[Creating a Unified
Recommender](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.
+3. A post describing the loglikelihood ratio: [Surprise and
Coinsidence](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.
Below are the command line jobs but the drivers and associated code can also
be customized and accessed from the Scala APIs.
