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https://issues.apache.org/jira/browse/MAHOUT-810?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13534749#comment-13534749
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sdfox commented on MAHOUT-810:
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Mr.Dogan, are you still working on the EnsembleRecommender? Can I do some work
for this project？
> 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
> Priority: Minor
> Fix For: Backlog
>
>
> 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
> praneetmha...@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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