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I'm talking about current winners in netflix prize. Their algorithms are a combination of hundreds individual recommenders blended together by another learning
by gaika 18y ago
I'm talking about current winners in netflix prize. Their algorithms are a combination of hundreds individual recommenders blended together by another learning algorithm. There's no easy way to explain the result, but I guess one can cheat and run a simpler version on top of it to produce an "explanation".
- elq 18y agoCorrect. When using a latent factor method, providing an explanation for for the prediction has been essentially intractable. A common post-processing step for many teams (particularly BellKor/KorBell) is a KNN. The KNN's are used to provide a sort of confidence metric and if the anyone so decided, using the KNN derived network for explanation is pretty simple and not really cheating. Edit: Yehuda might have made some breakthrough - He'll be presenting a paper at KDD "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", the paper will probably be released after the conference.
- carterschonwald 18y agoits already on his page http://www.research.att.com/~yehuda/index_pubs.html http://www.research.att.com/~yehuda/index_pubs.html 4th one down
- ericb 18y agoIt's interesting, because there's evidence that that's what we as humans do. The subconscious decides something, then we make up a story when asked why we did X.