513 lines
28 KiB
Markdown
513 lines
28 KiB
Markdown
---
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created_at: '2015-07-22T14:00:15.000Z'
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title: Netflix and the Napoleon Dynamite Problem (2008)
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url: http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html
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author: ImpressiveWebs
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points: 57
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story_text:
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comment_text:
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num_comments: 54
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story_id:
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story_title:
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story_url:
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parent_id:
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created_at_i: 1437573615
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_tags:
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- story
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- author_ImpressiveWebs
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- story_9929667
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objectID: '9929667'
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year: 2008
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---
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Bertoni says it’s partly because of “Napoleon Dynamite,” an indie comedy
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from 2004 that achieved cult status and went on to become extremely
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popular on Netflix. It is, Bertoni and others have discovered,
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maddeningly hard to determine how much people will like it. When Bertoni
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runs his algorithms on regular hits like “Lethal Weapon” or “Miss
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Congeniality” and tries to predict how any given Netflix user will rate
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them, he’s usually within eight-tenths of a star. But with films like
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“Napoleon Dynamite,” he’s off by an average of 1.2 stars.
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The reason, Bertoni says, is that “Napoleon Dynamite” is very weird and
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very polarizing. It contains a lot of arch, ironic humor, including a
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famously kooky dance performed by the titular teenage character to help
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his hapless friend win a student-council election. It’s the type of
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quirky entertainment that tends to be either loved or despised. The
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movie has been rated more than two million times in the Netflix
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database, and the ratings are disproportionately one or five stars.
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Worse, close friends who normally share similar film aesthetics often
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heatedly disagree about whether “Napoleon Dynamite” is a masterpiece or
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an annoying bit of hipster self-indulgence. When Bertoni saw the movie
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himself with a group of friends, they argued for hours over it. “Half of
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them loved it, and half of them hated it,” he told me. “And they
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couldn’t really say why. It’s just a difficult movie.”
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Mathematically speaking, “Napoleon Dynamite” is a very significant
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problem for the Netflix Prize. Amazingly, Bertoni has deduced that this
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single movie is causing 15 percent of his remaining error rate; or to
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put it another way, if Bertoni could anticipate whether you’d like
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“Napoleon Dynamite” as accurately as he can for other movies, this
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feat alone would bring him 15 percent of the way to winning the $1
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million prize. And while “Napoleon Dynamite” is the worst culprit, it
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isn’t the only troublemaker. A small subset of other titles have caused
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almost as much bedevilment among the Netflix Prize competitors. When
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Bertoni showed me a list of his 25 most-difficult-to-predict movies, I
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noticed they were all similar in some way to “Napoleon Dynamite” —
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culturally or politically polarizing and hard to classify, including “I
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Heart Huckabees,” “Lost in Translation,” “Fahrenheit 9/11,” “The Life
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Aquatic With Steve Zissou,” “Kill Bill: Volume 1” and “Sideways.”
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So this is the question that gently haunts the Netflix competition, as
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well as the recommendation engines used by other online stores like
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Amazon and iTunes. Just how predictable is human taste, anyway? And if
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we can’t understand our own preferences, can computers really be any
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better at it?
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**IT USED TO BE THAT** if you wanted to buy a book, rent a movie or shop
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for some music, you had to rely on flesh-and-blood judgment — yours, or
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that of someone you trusted. You’d go to your local store and look for
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new stuff, or you might just wander the aisles in what librarians call a
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stack search, to see if anything jumped out at you. You might check out
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newspaper reviews or consult your friends; if you were lucky, your local
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video store employed one of those young cinéastes who could size you up
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in a glance and suggest something suitable.
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The advent of online retailing completely upended this cultural and
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economic ecosystem. First of all, shopping over the Web is not a social
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experience; there are no clever clerks to ask for advice. What’s more,
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because they have no real space constraints, online stores like Amazon
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or iTunes can stock millions of titles, making a stack search
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essentially impossible. This creates the classic problem of choice: how
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do you decide among an effectively infinite number of options?
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Advertisement
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[Continue reading the main story](#story-continues-4)
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But Web sites have this significant advantage over brick-and-mortar
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stores: They can track everything their customers do. Every page you
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visit, every purchase you make, every item you rate — it is all
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recorded. In the early ’90s, scientists working in the field of “machine
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learning” realized that this enormous trove of data could be used to
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analyze patterns in people’s taste. In 1994, Pattie Maes, an M.I.T.
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professor, created one of the first recommendation engines by setting up
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a Web site where people listed songs and bands they liked. Her computer
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algorithm performed what’s known as collaborative filtering. It would
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take a song you rated highly, find other people who had also rated it
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highly and then suggest you try a song that those people also said they
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liked.
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“We had this realization that if we gathered together a really large
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group of people, like thousands or millions, they could help one another
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find things, because you can find patterns in what they like,” Maes told
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me recently. “It’s not necessarily the one, single smart critic that is
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going to find something for you, like, ‘Go see this movie, go listen to
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this band\!’ ”
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In one sense, collaborative filtering is less personalized than a store
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clerk. The clerk, in theory anyway, knows a lot about you, like your age
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and profession and what sort of things you enjoy; she can even read your
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current mood. (Are you feeling lousy? Maybe it’s not the day for
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“Apocalypse Now.”) A collaborative-filtering program, in contrast,
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knows very little about you — only what you’ve bought at a Web site and
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whether you rated it highly or not. But the computer has numbers on its
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side. It may know only a little bit about you, but it also knows a
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little bit about a huge number of other people. This lets it detect
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patterns we often cannot see on our own. For example, Maes’s
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music-recommendation system discovered that people who like classical
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music also like the Beatles. It is an epiphany that perhaps make sense
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when you think about it for a second, but it isn’t immediately obvious.
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Photo
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Soon after Maes’s work made its debut, online stores quickly understood
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the value of having a recommendation system, and today most Web sites
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selling entertainment products have one. Most of them use some variant
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of collaborative filtering — like Amazon’s “Customers Who Bought This
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Item Also Bought” function. Some setups ask you to actively rate
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products, as Netflix does. But others also rely on passive information.
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They keep track of your everyday behavior, looking for clues to your
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preferences. (For example, many music-recommendation engines — like the
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Genius feature on Apple’s iTunes, Microsoft’s Mixview music recommender
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or the Audioscrobbler program at Last.fm — can register every time you
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listen to a song on your computer or MP3 player.) And a few rare
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services actually pay people to evaluate products; the Pandora
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music-streaming service has 50 employees who listen to songs and tag
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them with descriptors — “upbeat,” “minor key,” “prominent vocal
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harmonies.”
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Netflix came late to the party. The company opened for business in 1997,
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but for the first three years it offered no recommendations. This wasn’t
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such a big problem when Netflix stocked only 1,000 titles or so, because
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customers could sift through those pretty quickly. But Netflix grew, and
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today, it stocks more than 100,000 movies. “I think that once you get
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beyond 1,000 choices, a recommendation system becomes critical,”
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Hastings, the Netflix C.E.O., told me. “People have limited cognitive
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time they want to spend on picking a movie.”
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Cinematch was introduced in 2000, but the first version worked poorly —
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“a mix of insightful and boneheaded recommendations,” according to
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Hastings. His programmers slowly began improving the algorithms. They
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could tell how much better they were getting by trying to replicate how
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a customer rated movies in the past. They took the customer’s ratings
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from, say, 2001, and used them to predict their ratings for 2002.
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Because Netflix actually had those later ratings, it could discern what
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a “perfect” prediction would look like. Soon, Cinematch reached the
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point where it could tease out some fairly nuanced — and surprising —
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connections. For example, it found that people who enjoy “The Patriot”
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also tend to like “Pearl Harbor,” which you’d expect, since they’re both
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history-war-action movies; but it also discovered that they like the
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heartstring-tugging drama “Pay It Forward” and the sci-fi movie “I,
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Robot.”
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Cinematch has, in fact, become a video-store roboclerk: its suggestions
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now drive a surprising 60 percent of Netflix’s rentals. It also often
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steers a customer’s attention away from big-grossing hits toward
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smaller, independent movies. Traditional video stores depend on hits;
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just-out-of-the-theaters blockbusters account for 80 percent of what
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they rent. At Netflix, by contrast, 70 percent of what it sends out is
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from the backlist — older movies or small, independent ones. A good
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recommendation system, in other words, does not merely help people find
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new stuff. As Netflix has discovered, it also spurs them to consume more
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stuff.
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For Netflix, this is doubly important. Customers pay a flat monthly
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rate, generally $16.99 (although cheaper plans are available), to check
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out as many movies as they want. The problem with this business model is
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that new members often have a couple of dozen movies in mind that they
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want to see, but after that they’re not sure what to check out next, and
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their requests slow. And a customer paying $17 a month for only one
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movie every month or two is at risk of canceling his subscription; the
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plan makes financial sense, from a user’s point of view, only if you
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rent a lot of movies. (My wife and I once quit Netflix for precisely
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this reason.) Every time Hastings increases the quality of Cinematch
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even slightly, it keeps his customers active.
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Advertisement
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[Continue reading the main story](#story-continues-5)
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But by 2006, Cinematch’s improving performance had plateaued. Netflix’s
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programmers couldn’t go any further on their own. They suspected that
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there was a big breakthrough out there; the science of recommendation
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systems was booming, and computer scientists were publishing hundreds of
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papers each year on the subject. At a staff meeting in the summer of
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2006, Hastings suggested a radical idea: Why not have a public contest?
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Netflix’s recommendation system was powered by the wisdom of crowds; now
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it would tap the wisdom of crowds to get better too.
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**AS HASTINGS HOPED**, the contest has galvanized nerds around the
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world. The Top 10 list for the Netflix Prize currently includes a group
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of programmers in Austria (who are at No. 2), a trained psychologist and
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Web consultant in Britain who uses his teenage daughter to perform his
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calculus (No. 9), a lone Ph.D. candidate in Boston who calls himself My
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Brain and His Chain (a reference to a Ben Folds song; he’s at No. 6) and
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Pragmatic Theory — two French-Canadian guys in Montreal (No. 3). Nearly
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every team is working on the prize in its spare time. In October, when I
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dropped by the house of Martin Chabbert, a 32-year-old member of the
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Pragmatic Theory duo, it was only 8:30 at night, but we had to whisper:
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his four children, including a 2-month-old baby, had just gone to bed
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upstairs. In his small dining room, a laptop sat open next to children’s
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books like “Les Robots: Au Service de L’homme” and a “Star Wars” picture
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book in French.
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“This is where I do everything,” Chabbert said. “After the kids are
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asleep and I’ve packed the lunches for school, I come down at 9 in the
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evening and work until 11 or 12. It was very exciting in the
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beginning\!” He laughed. “It still is, but with the baby now, going to
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bed at midnight is not a good idea.”
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Pragmatic Theory formed last spring, when Chabbert’s longtime friend
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Martin Piotte — a 43-year-old electrical and computer engineer — heard
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about the Netflix Prize. Like many of the amateurs trying to win the $1
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million, they had no relevant expertise. (“Absolutely no background in
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statistics that was useful,” Piotte told me ruefully. “Two guys,
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absolutely no clue.”) But they soon discovered that the Netflix
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competition is a fairly collegial affair. The company hosts a discussion
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board devoted to the prize, and competitors frequently help one another
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out — discussing algorithms they’ve tried and publicly brainstorming new
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ways to improve their work, sometimes even posting reams of computer
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code for anyone to use. When someone makes a breakthrough, pretty soon
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every other team is aware of it and starts using it, too. Piotte and
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Chabbert soon learned the major mathematical tricks that had propelled
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the leading teams into the Top 10.
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## Newsletter Sign Up
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[Continue reading the main story](#continues-post-newsletter)
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[View all New York Times newsletters.](/newsletters)
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The first major breakthrough came less than a month into the
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competition. A team named Simon Funk vaulted from nowhere into the No. 4
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position, improving upon Cinematch by 3.88 percent in one fell swoop.
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Its secret was a mathematical technique called singular value
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decomposition. It isn’t new; mathematicians have used it for years to
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make sense of prodigious chunks of information. But Netflix never
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thought to try it on movies.
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Singular value decomposition works by uncovering “factors” that Netflix
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customers like or don’t like. Say, for example, that “Sleepless in
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Seattle” has been rated by 200,000 Netflix users. In one sense, this is
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just a huge list of numbers — user No. 452 gave it two stars; No. 985
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gave it five stars; and so on. But you could also think of those ratings
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as individual reactions to various aspects of the movie. “Sleepless in
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Seattle” is a “chick flick,” a comedy, a star vehicle for Tom Hanks;
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each customer is reacting to how much — or how little — he or she likes
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“chick flicks,” comedies and Tom Hanks. Singular value decomposition
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takes the mass of Netflix data — 17,770 movies, ratings by 480,189 users
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— and automatically sorts the films. The programmers do not actively
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tell the computer what to look for; they just run the algorithm until it
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groups together movies that share qualities with predictive value.
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Sometimes when you look at the clusters of movies, you can deduce the
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connections. Chabbert showed me one list: at the top were “Sleepless in
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Seattle,” “Steel Magnolias” and “Pretty Woman,” while at the bottom were
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“Star Trek” movies. Clearly, the computer recognized some factor that
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suggests that someone who likes the romantic aspect of “Pretty Woman”
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will probably like “Sleepless in Seattle” and dislike “Star Trek.”
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Chabbert showed me another cluster: this time DVD collections of the TV
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show “Friends” all clustered at the top of the list, while action movies
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like “Reindeer Games” and thrillers like “Hannibal” clustered at the
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bottom. Most likely, the computer had selected for “comic” content here.
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Other lists appear to group movies based on whether they lean strongly
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to the ideological right or left.
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As programmers extract more and more values, it becomes possible to draw
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exceedingly sophisticated correlations among movies and hence to offer
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incredibly nuanced recommendations. “We’re teasing out very subtle human
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behaviors,” said Chris Volinsky, a scientist with AT\&T in New Jersey
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who is one of the most successful Netflix contestants; his three-person
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team held the No. 1 position for more than a year. His team relies, in
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part, on singular value decomposition. “You can find things like ‘People
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who like action movies, but only if there’s a lot of explosions, and not
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if there’s a lot of blood. And maybe they don’t like profanity,’ ”
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Volinsky told me when we spoke recently. “Or it’s like ‘I like action
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movies, but not if they have Keanu Reeves and not if there’s a bus
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involved.’ ”
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**MOST OF THE LEADING TEAMS** competing for the Netflix Prize now use
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singular value decomposition. Indeed, given how quickly word of new
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breakthroughs spreads among the competitors, virtually every team in the
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Top 10 makes use of similar mathematical ploys. The only thing that
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separates their scores is how skillfully they tweak their algorithms.
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The Netflix Prize has come to resemble a drag race in which everyone
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drives the same car, with only tiny modifications to the fuel injection.
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Yet those tweaks are crucial. Since the top teams are so close — there
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is less than a tenth of a percent between each contender — even tiny
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improvements can boost a team to the top of the charts.
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Advertisement
|
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[Continue reading the main story](#story-continues-6)
|
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These days, the competitors spend much of their time thinking deeply
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about the math and psychology behind recommendations. For example, the
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teams are grappling with the problem that over time, people can change
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how sternly or leniently they rate movies. Psychological studies show
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that if you ask someone to rate a movie and then, a month later, ask him
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to do so again, the rating varies by an average of 0.4 stars. “The
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question is why,” Len Bertoni said to me. “Did you just remember it
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differently? Did you see something in between? Did something change in
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your life that made you rethink it?” Some teams deal with this by
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programming their computers to gradually discount older ratings.
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Another common problem is identifying overly punitive raters. If you’re
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a really harsh critic and I’m a much more easygoing one, your two-star
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rating may be equal to my four-star rating. To compensate, an algorithm
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might try to detect when a Netflix customer tends to hand out only one-
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or two-star ratings — a sign of a strict, pursed-lip customer — and
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artificially boost his or her ratings by a half-star or so. Then there’s
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the problem of movie raters who simply aren’t consistent. They might be
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evenhanded most of the time, but if they log into Netflix when they’re
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in a particularly bad mood, they might impulsively decide to rate a
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couple of dozen movies harshly.
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Photo
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TV shows, which are hot commodities on Netflix, present yet another
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perplexing issue. Customers respond to TV series much differently than
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they do to movies. People who loved the first two seasons of “The Wire”
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might start getting bored during the third but keep on watching for a
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while, then stop abruptly. So when should Cinematch stop recommending
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“The Wire”? When do you tell someone to give up on a TV show?
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Interestingly, the Netflix Prize competitors do not know anything about
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the demographics of the customers whose taste they’re trying to predict.
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The teams sometimes argue on the discussion board about whether their
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predictions would be better if they knew that customer No. 465 is, for
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example, a 23-year-old woman in Arizona. Yet most of the leading teams
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say that personal information is not very useful, because it’s too
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crude. As one team pointed out to me, the fact that I’m a 40-year-old
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West Village resident is not very predictive. There’s little reason to
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think the other 40-year-old men on my block enjoy the same movies as I
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do. In contrast, the Netflix data are much more rich in meaning. When I
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tell Netflix that I think Woody Allen’s black comedy “Match Point”
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deserves three stars but the Joss Whedon sci-fi film “Serenity” is a
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five-star masterpiece, this reveals quite a lot about my taste. Indeed,
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Reed Hastings told me that even though Netflix has a good deal of
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demographic information about its users, the company does not currently
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use it much to generate movie recommendations; merely knowing who people
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are, paradoxically, isn’t very predictive of their movie tastes.
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As the teams have grown better at predicting human preferences, the more
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incomprehensible their computer programs have become, even to their
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creators. Each team has lined up a gantlet of scores of algorithms, each
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one analyzing a slightly different correlation between movies and users.
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The upshot is that while the teams are producing ever-more-accurate
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recommendations, they cannot precisely explain how they’re doing this.
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Chris Volinsky admits that his team’s program has become a black box,
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its internal logic unknowable.
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There’s a sort of unsettling, alien quality to their computers’ results.
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When the teams examine the ways that singular value decomposition is
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slotting movies into categories, sometimes it makes sense to them — as
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when the computer highlights what appears to be some essence of
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nerdiness in a bunch of sci-fi movies. But many categorizations are now
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so obscure that they cannot see the reasoning behind them. Possibly the
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algorithms are finding connections so deep and subconscious that
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customers themselves wouldn’t even recognize them. At one point,
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Chabbert showed me a list of movies that his algorithm had discovered
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share some ineffable similarity; it includes a historical movie, “Joan
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of Arc,” a wrestling video, “W.W.E.: SummerSlam 2004,” the comedy “It
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Had to Be You” and a version of Charles Dickens’s “Bleak House.” For the
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life of me, I can’t figure out what possible connection they have, but
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Chabbert assures me that this singular value decomposition scored 4
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percent higher than Cinematch — so it must be doing something right. As
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Volinsky surmised, “They’re able to tease out all of these things that
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we would never, ever think of ourselves.” The machine may be
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understanding something about us that we do not understand ourselves.
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Yet it’s clear that something is still missing. Volinsky’s momentum has
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slowed down significantly, as everyone else’s has. There’s some X factor
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in human judgment that the current bunch of algorithms isn’t capturing
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when it comes to movies like “Napoleon Dynamite.” And the problem looms
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large. Bertoni is currently at 8.8 percent; he says that a small group
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of mainly independent movies represents more than half of the remaining
|
||
errors in the way of winning the prize. Most teams suspect that
|
||
continuing to tweak existing algorithms won’t be enough to get to 10
|
||
percent. They need another breakthrough — some way to digitally
|
||
replicate the love/hate dynamic that governs hard-to-pigeonhole indie
|
||
films.
|
||
|
||
“This last half-percent really is the Mount Everest,” Volinsky said.
|
||
“It’s going to take one of these ‘aha’ moments.”
|
||
|
||
Advertisement
|
||
|
||
[Continue reading the main story](#story-continues-7)
|
||
|
||
**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](http://topics.nytimes.com/top/reference/timestopics/subjects/s/sex_and_the_city/index.html?inline=nyt-classifier "More articles about Sex and the City.")’
|
||
to me, I’m glad I saw it, because now I get it. I know all the
|
||
in-jokes.”
|
||
|
||
Maes suspects that in the future, computer-based reasoning will become
|
||
less important for online retailers than social-networking tools that
|
||
tap into the social zeitgeist, that let customers see, in Facebook
|
||
fashion, for example, what their close friends are watching and buying.
|
||
(Potter has an even more intriguing idea. He says he thinks that a
|
||
recommendation system could predict cultural microtrends by monitoring
|
||
news events. His research has found, for example, that people rent more
|
||
movies about Wall Street when the stock market drops.) In the world of
|
||
music, there are already several innovative recommendation services that
|
||
try to analyze buzz — by monitoring blogs for repeated mentions of
|
||
up-and-coming bands, or by sifting through millions of people’s
|
||
playlists to see if a new band is suddenly getting a lot of attention.
|
||
|
||
Of course, for a company like Netflix, there’s a downside to pushing
|
||
exciting-but-risky movie recommendations on viewers. If Netflix tries to
|
||
stretch your taste by recommending more daring movies, it also risks
|
||
annoying customers. A bad movie recommendation can waste an evening.
|
||
|
||
Is there any way to find a golden mean? When I put the question to Reed
|
||
Hastings, the Netflix C.E.O., he told me he suspects that there won’t be
|
||
any simple answer. The company needs better algorithms; it needs
|
||
breakthrough techniques like singular value decomposition, with the
|
||
brilliant but inscrutable insights it enables. But Hastings also says he
|
||
thinks Maes is right, too, and that social-networking tools will become
|
||
more useful. (Netflix already has one, in fact — an application that
|
||
lets users see what their family and peers are renting. But Hastings
|
||
admits it hasn’t been as valuable as computerized intelligence; only a
|
||
very small percentage of rentals are driven by what friends have
|
||
chosen.) Hastings is even considering hiring cinephiles to watch all
|
||
100,000 movies in the Netflix library and write up, by hand, pages of
|
||
adjectives describing each movie, a cloud of tags that would offer a
|
||
subjective view of what makes films similar or dissimilar. It might
|
||
imbue Cinematch with more unpredictable, humanlike intelligence.
|
||
|
||
Advertisement
|
||
|
||
[Continue reading the main story](#story-continues-8)
|
||
|
||
“Human beings are very quirky and individualistic, and wonderfully
|
||
idiosyncratic,” Hastings says. “And while I love that about human
|
||
beings, it makes it hard to figure out what they like.”**
|
||
|
||
[Continue reading the main story](#whats-next)
|