http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?pagewanted=all&_r=0
http://genresofnetflix.tumblr.com
http://www.netflixprize.com
As just one of the millions of Netflix subscribers and self-diagnosed binger, I have definitely spent many long nights getting familiarized with the altgenre system implemented into the streaming media site. I’ve been avidly using Netflix since 2011, but I’ve only really started taking notice of some of its extremely specific genres until just this year. With thousands of titles to sort through on Netflix, their personalized genres are definitely useful, maybe a bit absurd, but still useful. My personal favorites are “hidden gems” or “visually-striking movies” where in these categories I can usually find many independent and quirky films that are difficult to describe.
It is apparent that there is a growing trend of implementing these personalizing algorithms into more and more media sources including the likes of Spotify, Amazon, and Soundcloud. What I find most intriguing and even slightly disturbing about Netflix’s system is the crossover of both human and machine intelligence. It has come to the point where you can probably learn a lot about a person’s interests by simply looking through their Netflix account. In order to achieve this, Netflix engineers definitely had a strong input in creating micro tags for these films based on the Netflix Quantum Theory which makes me question how objective the algorithm system remains to be. There seems to be an ideology similar to the whole “I know it when I see it” expression that is crossing into Netflix’s system.
Netflix has evolved their past system that was based more heavily on numerical values and user ratings to a more human method of introspection. Todd Yellin, VP of product innovation at Netflix had this to say about their new approach:
“Predicting something is 3.2 stars is kind of fun if you have an engineering sensibility, but it would be more useful to talk about dysfunctional families and viral plagues. We wanted to put in more language,”
I think it was a very progressive approach for Netflix that also reveals some very interesting quirks about the relationship between categorizing systems and human nature. Atlantic’s article on Netflix’s genre algorithm system mentions the $1 million prize that the company had offered back in 2006 which reminded me of the Napoleon Dynamite problem. As a film, Napoleon Dynamite seems to be the most difficult movie to pinpoint and recommend to Netflix users. The quirky film remains to be the most stubbornly unpredictable movie as it is attracts many users to rate the film while still being hard to predict. This imbalance, while probably a headache for Netflix developers and engineers, is to me a very humorous quirk in the system that shows how difficult it is to categorize human interests and behaviors.