Analyzing Netflix’s thousands of movie genres

Alexis Madrigal decided to look into the movie genres of Netflix – and found lots of interesting data:

As the hours ticked by, the Netflix grammar—how it pieced together the words to form comprehensible genres—began to become apparent as well.

If a movie was both romantic and Oscar-winning, Oscar-winning always went to the left: Oscar-winning Romantic Dramas. Time periods always went at the end of the genre: Oscar-winning Romantic Dramas from the 1950s

In fact, there was a hierarchy for each category of descriptor. Generally speaking, a genre would be formed out of a subset of these components:

Region + Adjectives + Noun Genre + Based On… + Set In… + From the… + About… + For Age X to Y

Yellin said that the genres were limited by three main factors: 1) they only want to display 50 characters for various UI reasons, which eliminates most long genres; 2) there had to be a “critical mass” of content that fit the description of the genre, at least in Netflix’s extended DVD catalog; and 3) they only wanted genres that made syntactic sense.

And the conclusion is that there are so many genres that they don’t necessarily make sense to humans. This strikes me as a uniquely modern problem: we know how to find patterns via algorithm and then we have to decide whether we want to know why the patterns exist. We might call this the Freakonomics problem: we can collect reams of data, data mine it, and then have to develop explanations. This, of course, is the reverse of the typical scientific process that starts with theories and then goes about testing them. The Netflix “reverse engineering” can be quite useful but wouldn’t it be nice to know why Perry Mason and a few other less celebrated actors show up so often?

At the least, I bet Hollywood would like access to such explanations. This also reminds me of the Music Genome Project that underlies Pandora. Unlock the genres and there is money to be made.

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