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Metadata is useful when you need to interpret the results, and most people care about why something is recommended to them. Top netflix algorithms are black box
by gaika 18y ago
Metadata is useful when you need to interpret the results, and most people care about why something is recommended to them. Top netflix algorithms are black boxes from the user standpoint, that doesn't help when making a decision to buy or rent based on that recommendation.
Compare that with Amazon's approach when they sacrifice the predictive power for the useful explanation (customers who bought X also bought Y)
- ehedberg 18y agoAmazon isn't sacrificing predictive power because they want to give you the explanation. They're just including profit margin in the data to give recommendations that are valuable to them. Not that I can blame them.
- wmf 18y agoBut Netflix does say that. e.g.: Twin Peaks: Season 1 (3-Disc Series) Because you enjoyed: Mulholland Drive Brazil Videodrome So right away I know that Twin Peaks is pretty freaky.
- gaika 18y agoI'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.
- greendestiny 18y agoI don't know you can say Amazon is sacrificing any power. The Netflix prize isn't about better recommendations per se, rather its about predicting users movie ratings. Its not clear predicting these ratings better will give better recommendations. I think most of the component algorithms in the Bell labs method provide fairly straight forward explanations - the best predictor is a nearest neighbour algorithm.
- andrewf 18y agoI wonder if, having used an impossible-to-explain algorithm to arrive at your solution, you could compare your solution against a few very simple algorithms, and offer up the best fit as a rationalization.
- byrneseyeview 18y agoFine, but if anyone copyrights normal human reasoning, you're in trouble.
- wheels 18y agoLegal nitpick: You meant patents. Patents are for processes, copyright is for instances.
- byrneseyeview 18y agoYou're right. Thanks for the correction.