On Friday, Netflix announced on its corporate blog that it has settled a lawsuit related to its Netflix Prize, a $1 million contest that challenged machine learning experts to use Netflix's data to produce better recommendations than the movie giant could serve up themselves.
The lawsuit called attention to academic research that suggests that Netflix indirectly exposed the movie preferences of its users by publishing anonymized customer data. In the suit, plaintiff Paul Navarro and others sought an injunction preventing Netflix from going through the so-called "Netflix Prize II," a follow-up challenge that Netflix promised would offer up even more personal data such as genders and zipcodes.
"Netflix is not going to pursue a sequel to the Netflix Prize," says spokesman Steve Swasey. "We looked at this, we heard some dissension and so we've settled it, resolved the issues and are moving on."
The movie rental company captured the world's attention when it announced the Netflix Prize in October 2006, offering a reward to anyone who could improve upon Netflix's personalized movie recommendations by a 10% margin. That mandate wasn't achieved for close to three years and, at one point, was generally thought to be impossible.While it's a shame there won't be another round, this position is completely understandable. While the research and academic value of available large datasets is very high, the challenges with protecting consumer privacy are incredibly difficult to overcome for all cases.
My favorite story from the first competition was covered in a NYT article titled If You Liked This, You're Sure to Love That which described the 'Napoleon Dynamite problem':
Bertoni says [getting closer to the prize target is harder] partly because of “Napoleon Dynamite,” an indie comedy from 2004 that achieved cult status and went on to become extremely popular on Netflix. It is, Bertoni and others have discovered, maddeningly hard to determine how much people will like it. When Bertoni runs his algorithms on regular hits like “Lethal Weapon” or “Miss Congeniality” and tries to predict how any given Netflix user will rate them, he’s usually within eight-tenths of a star. But with films like “Napoleon Dynamite,” he’s off by an average of 1.2 stars.And that pretty much summarizes the fun problems in data mining and intent prediction and the reasons I get excited about this stuff.
The reason, Bertoni says, is that “Napoleon Dynamite” is very weird and very polarizing. It contains a lot of arch, ironic humor, including a famously kooky dance performed by the titular teenage character to help his hapless friend win a student-council election. It’s the type of quirky entertainment that tends to be either loved or despised. The movie has been rated more than two million times in the Netflix database, and the ratings are disproportionately one or five stars.
Worse, close friends who normally share similar film aesthetics often heatedly disagree about whether “Napoleon Dynamite” is a masterpiece or an annoying bit of hipster self-indulgence. When Bertoni saw the movie himself with a group of friends, they argued for hours over it. “Half of them loved it, and half of them hated it,” he told me. “And they couldn’t really say why. It’s just a difficult movie.”