Type: New Feature
Resolution: Won't Fix
Affects Version/s: None
Fix Version/s: None
Component/s: Collaborative Filtering
Q: Is there an EnsembleRecommender or CompoundRecommender that takes input
from other recommender algorithms and combine them to generate better
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
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".
In terms of papers about ensemble methods/blending I suggest looking at the
BigChaos Netflix paper:
See section 7.