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From "Daniel Xiaodan Zhou (JIRA)" <j...@apache.org>
Subject [jira] [Created] (MAHOUT-810) Create EnsembleRecommender
Date Wed, 14 Sep 2011 14:24:16 GMT
Create EnsembleRecommender
--------------------------

                 Key: MAHOUT-810
                 URL: https://issues.apache.org/jira/browse/MAHOUT-810
             Project: Mahout
          Issue Type: New Feature
          Components: Collaborative Filtering
            Reporter: Daniel Xiaodan Zhou
            Assignee: Sean Owen
            Priority: Minor


Q: Is there an EnsembleRecommender or CompoundRecommender that takes input
from other recommender algorithms and combine them to generate better
results? 

Ted Dunning:
There isn't really any such thing although the SGD models are easy to glue
together in this way.
There is a guy named Praneet at UCI who is doing some feature sharding work
that might relate to what you are doing.  His email is
praneetmhatre@gmail.com

Sean Owen:
There isn't. For the recommenders that work by computing an estimated
preference value for items, I suppose you could average their
estimates and rank by that.
More crudely, you could stitch together the recommendations of
recommender 1 and 2 by taking the top 10 amongst each of their top
recommendations -- averaging estimates where an item appears in both
lists. That's much less work for you; it's not quite as "accurate".

Danny Bickson:
In terms of papers about ensemble methods/blending I suggest looking at the
BigChaos Netflix paper:
http://www.*netflixprize*.com/assets/*GrandPrize2009*_BPC_*BigChaos*.pdf
See section 7.


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