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
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.
Best,
Danny Bickson
On Wed, Sep 14, 2011 at 11:41 AM, Sean Owen sro...@gmail.com wrote:
There isn't. For
Thanks for all the suggestions. I have created an issue in Jira. Will work on
it soon.
On Sep 14, 2011, at 5:00 AM, Danny Bickson wrote:
In terms of papers about ensemble methods/blending I suggest looking at the
BigChaos Netflix paper:
Is there an EnsembleRecommender or CompoundRecommender that takes input from
other recommender algorithms and combine them to generate better results? If
not, I'm thinking to contribute a patch. Any suggestions on implementation?
Thanks.
Daniel Zhou
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
On Wed, Sep 14, 2011 at 1:39 AM, Daniel
