This was a very comprehensive article that triggered a couple examples of other generators I encountered after reading it. Near the beginning of his piece, Madrigal had an interesting point about tracking the URL and its incremental values at the end of the web address. I’ve also been able to navigate between pages using that same line of logic. It strikes me that for some databases, a lot is revealed to the public, while others are much more privatized depending on how the system was created in the first place. Once you discover the thought process behind the ways some things are categorized, you can easily find what you’re looking for in a general sense.
After playing with the generator for a few minutes, I started to wonder if any director will rise up to the challenge and actually create a movie based on the results of this generator. Maybe that way, we’ll be able to see more original movies. I’ve always believed in the notion that people can do great things once they’re given some limitations. It’s an intriguing thought that something original can be made based on unoriginal words, descriptions, and genres produced by algorithms. This truly showcases the wide range of possibilities that any given combination can produce.
I also love this quote from the article, “It’s where the human intelligence of the taggers gets combined with the machine intelligence of the algorithms. There’s something in the Netflix personalized genres that I think we can tell is not fully human, but is revealing in a way that humans alone might not be.” In a very digital humanitarian sense, this project was able to produce many eye-opening graphs that gives the public an inside look to what types of things human beings prefer just by analyzing and presenting the data a different way. Instead of being recommended different genres and searching through them, we are now able to generate our own and the results that pop up says a lot about our diverse preferences and creativity of past directors that have shaped the movie industry.
Because of how many times Spotify was mentioned in the class, the first thing I did after reading the article was to google for a ‘Spotify based music genre generator’. What I found was this playlist generator site linked to Spotify that lets you search for playlists that were made from other users and were categorized by either mood or genre. It’s unfortunate that you can’t search for both simultaneously, but when creating a playlist, you can tag descriptions under both types of categories. Another site that I encountered was a more minimalist music genre generator that operated under a similar idea as the Netflix one in that it combined a couple of descriptive words from a database to create new music genres. Lastly, I found an actual site that lets you generate your own generators. Even though there was a user-created movie genre generator, it only allowed you to mash together two random genres and I’m willing to bet that the database it was pulling the genres from is a lot less detailed than the one Yellin created under Netflix.
Sites Used in Order of Appearance:
http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/6/
http://playlists.net/
http://jbdowse.com/genres
http://www.generatorland.com/