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Old 05-05-2012, 08:07 AM
mjbeeson mjbeeson is offline
Join Date: Apr 2012
Posts: 25
Default Netflix movie recommendations

According to lecture, each movie and each person is described by a vector of numbers; each number measures a "feature" that a movie can have (to some degree between 0 and 1), such as "comedy", or "dark", or "witty". ( I see these features mentioned by name in my Netflix recommendations.) Then the coefficients describing each movie and each person are adjusted by machine learning. But what I don't understand yet is where these features come from. Did a human invent them? or did a machine algorithm invent them?

Apparently in the banking example and the digit-recognition example, the key "features" were invented by humans, although, the last lecture hinted that the features in the banking example may have been machine-invented.
Feature-invention seems like a very key step in developing a particular application, so if there is a way to have the machine invent just the right features, that would be very interesting. I don't see anything in the textbook on this issue (there is an index entry for "feature selection", but it leads to p. 151 where the only mention of features is in the last few lines, about the digit-recognition example).

For example: Is there any algorithm that would take the raw data for the digit-recognition example and come up with the useful features? Also, a fingerprint-recognition example was briefly discussed, but the required features were not mentioned. What were they? and how were they identified? I am old enough to remember the time before computers could recognize fingerprints! Then it was considered a very difficult problem.
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