Author: pat
Date: Sat Feb 14 17:14:36 2015
New Revision: 1659818
URL: http://svn.apache.org/r1659818
Log:
CMS commit to mahout by pat
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=1659818&r1=1659817&r2=1659818&view=diff
==============================================================================
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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 14 17:14:36 2015
@@ -14,10 +14,11 @@ recommendations and when paired with a s
##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 Unified
Recommender](http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/)
+2. A slide deck, which talks about mixing user actions and other indicators:
[Multimodal Streaming
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
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.
+4. 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.
+5. A demo [Video Guide][1] site, which uses many of the techniques described
above.
Below are the command line jobs but the drivers and associated code can also
be customized and accessed from the Scala APIs.
@@ -434,3 +435,6 @@ This will return recommendations favorin
2. Content can be used where there is no recorded user behavior or when items
change too quickly to get much interaction history. They can be used alone or
mixed with other indicators.
3. Most search engines support "boost" factors so you can favor one or more
indicators. In the example query, if you want tags to only have a small effect
you could boost the CF indicators.
4. In the examples we have used space delimited strings for lists of IDs in
indicators and in queries. It may be better to use arrays of strings if your
storage system and search engine support them. For instance Solr allows
multi-valued fields, which correspond to arrays.
+
+
+ [1]: https://guide.finderbots.com
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