The Napoleon Dynamite Problem

Who knew a ten percent improvement was so difficult?

THE “NAPOLEON DYNAMITE” problem is driving Len Bertoni crazy. Bertoni is a 51-year-old “semiretired” computer scientist who lives an hour outside Pittsburgh. In the spring of 2007, his sister-in-law e-mailed him an intriguing bit of news: Netflix, the Web-based DVD-rental company, was holding a contest to try to improve Cinematch, its “recommendation engine.” The prize: $1 million.

Cinematch is the bit of software embedded in the Netflix Web site that analyzes each customer’s movie-viewing habits and recommends other movies that the customer might enjoy. (Did you like the legal thriller “The Firm”? Well, maybe you’d like “Michael Clayton.” Or perhaps “A Few Good Men.”) The Netflix Prize goes to anyone who can make Cinematch’s predictions 10 percent more accurate. One million dollars might sound like an awfully big prize for such a small improvement. But in fact, Netflix’s founders tried for years to improve Cinematch, with only incremental results, and they knew that a 10 percent bump would be a challenge for even the most deft programmer. They also knew that, as Reed Hastings, the chief executive of Netflix, told me recently, “getting to 10 percent would certainly be worth well in excess of $1 million” to the company. The competition was announced in October 2006, and no one has won yet, though 30,000 hackers worldwide are hard at work on the problem. Each day, teams submit their updated solutions to the Netflix Prize Web page, and Netflix instantly calculates how much better than Cinematch they are.

But his progress had slowed to a crawl. The more Bertoni improved upon Netflix, the harder it became to move his number forward. This wasn’t just his problem, though; the other competitors say that their progress is stalling, too, as they edge toward 10 percent. Why?

Bertoni says it’s 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.

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.

[Continue reading The Screens Issue – If You Liked This, Sure to Love That – Winning the Netflix Prize –]

I’ve never watched Napoleon Dynamite [Netflix], but currently the Netflix rating system thinks I might like it:

Average of raters like you: 3.3 stars, from your 1198 ratings.

I’m skeptical, I could only watch the first reel1 of Anchorman: The Legend of Ron Burgundy [Netflix]. From my perspective, Napoleon Dynamite is from the same mold of satire, and not something I much care for.

Netflix Cracked

Anyway, I use the Netflix suggestion engine sometimes, but depend more upon other sources2 to keep my queue stuffed with possibilities.

Some Computer Scientists think the “Napoleon Dynamite” problem exposes a serious weakness of computers. They cannot anticipate the eccentric ways that real people actually decide to take a chance on a movie.

The Cinematch system, like any recommendation engine, assumes that your taste is static and unchanging. The computer looks at all the movies you’ve rated in the past, finds the trend and uses that to guide you. But the reality is that our cultural tastes evolve, and they change in part because we interact with others. You hear your friends gushing about “Mad Men,” so eventually — even though you have never had any particular interest in early-’60s America — you give it a try. Or you go into the video store and run into a particularly charismatic clerk who persuades you that you really, really have to give “The Life Aquatic With Steve Zissou” a chance.

As Gavin Potter, a Netflix Prize competitor who lives in Britain and is currently in ninth place, pointed out to me, a computerized recommendation system seeks to find the common threads in millions of people’s recommendations, so it inherently avoids extremes. Video-store clerks, on the other hand, are influenced by their own idiosyncrasies. Even if they’re considering your taste to make a suitable recommendation, they can’t help relying on their own sense of what’s good and bad. They’ll make more mistakes than the Netflix computers — but they’re also more likely to have flashes of inspiration, like pointing you to “Napoleon Dynamite” at just the right moment.

“If you use a computerized system based on ratings, you will tend to get very relevant but safe answers,” Potter says. “If you go with the movie-store clerk, you will get more unpredictable but potentially more exciting recommendations.”

Another critic of computer recommendations is, oddly enough, Pattie Maes, the M.I.T. professor. She notes that there’s something slightly antisocial — “narrow-minded” — about hyperpersonalized recommendation systems. Sure, it’s good to have a computer find more of what you already like. But culture isn’t experienced in solitude. We also consume shows and movies and music as a way of participating in society. That social need can override the question of whether or not we’ll like the movie.

“You don’t want to see a movie just because you think it’s going to be good,” Maes says. “It’s also because everyone at school or work is going to be talking about it, and you want to be able to talk about it, too.” Maes told me that a while ago she rented a “Sex and the City” DVD from Netflix. She suspected she probably wouldn’t really like the show. “But everybody else was constantly talking about it, and I had to know what they were talking about,” she says. “So even though I would have been embarrassed if Netflix suggested ‘Sex and the City’ to me, I’m glad I saw it, because now I get it. I know all the in-jokes.”

  1. a reel is usually the first 20 pages a script, translating to about 20 minutes of film, and usually sets up the story, basic characters, protagonist, yadda yadda []
  2. film reviews, suggestions of friends, blogs, those inevitable lists of best-of, and my own fluctuating interests []

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