---
created_at: '2015-07-22T14:00:15.000Z'
title: Netflix and the Napoleon Dynamite Problem (2008)
url: http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html
author: ImpressiveWebs
points: 57
story_text: 
comment_text: 
num_comments: 54
story_id: 
story_title: 
story_url: 
parent_id: 
created_at_i: 1437573615
_tags:
- story
- author_ImpressiveWebs
- story_9929667
objectID: '9929667'
year: 2008

---
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.

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.”

Mathematically speaking, “Napoleon Dynamite” is a very significant
problem for the Netflix Prize. Amazingly, Bertoni has deduced that this
single movie is causing 15 percent of his remaining error rate; or to
put it another way, if Bertoni could anticipate whether you’d like
“Napoleon Dynamite” as accurately as he can for other movies, this
feat alone would bring him 15 percent of the way to winning the $1
million prize. And while “Napoleon Dynamite” is the worst culprit, it
isn’t the only troublemaker. A small subset of other titles have caused
almost as much bedevilment among the Netflix Prize competitors. When
Bertoni showed me a list of his 25 most-difficult-to-predict movies, I
noticed they were all similar in some way to “Napoleon Dynamite” —
culturally or politically polarizing and hard to classify, including “I
Heart Huckabees,” “Lost in Translation,” “Fahrenheit 9/11,” “The Life
Aquatic With Steve Zissou,” “Kill Bill: Volume 1” and “Sideways.”

So this is the question that gently haunts the Netflix competition, as
well as the recommendation engines used by other online stores like
Amazon and iTunes. Just how predictable is human taste, anyway? And if
we can’t understand our own preferences, can computers really be any
better at it?

**IT USED TO BE THAT** if you wanted to buy a book, rent a movie or shop
for some music, you had to rely on flesh-and-blood judgment — yours, or
that of someone you trusted. You’d go to your local store and look for
new stuff, or you might just wander the aisles in what librarians call a
stack search, to see if anything jumped out at you. You might check out
newspaper reviews or consult your friends; if you were lucky, your local
video store employed one of those young cinéastes who could size you up
in a glance and suggest something suitable.

The advent of online retailing completely upended this cultural and
economic ecosystem. First of all, shopping over the Web is not a social
experience; there are no clever clerks to ask for advice. What’s more,
because they have no real space constraints, online stores like Amazon
or iTunes can stock millions of titles, making a stack search
essentially impossible. This creates the classic problem of choice: how
do you decide among an effectively infinite number of options?

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But Web sites have this significant advantage over brick-and-mortar
stores: They can track everything their customers do. Every page you
visit, every purchase you make, every item you rate — it is all
recorded. In the early ’90s, scientists working in the field of “machine
learning” realized that this enormous trove of data could be used to
analyze patterns in people’s taste. In 1994, Pattie Maes, an M.I.T.
professor, created one of the first recommendation engines by setting up
a Web site where people listed songs and bands they liked. Her computer
algorithm performed what’s known as collaborative filtering. It would
take a song you rated highly, find other people who had also rated it
highly and then suggest you try a song that those people also said they
liked.

“We had this realization that if we gathered together a really large
group of people, like thousands or millions, they could help one another
find things, because you can find patterns in what they like,” Maes told
me recently. “It’s not necessarily the one, single smart critic that is
going to find something for you, like, ‘Go see this movie, go listen to
this band\!’ ”

In one sense, collaborative filtering is less personalized than a store
clerk. The clerk, in theory anyway, knows a lot about you, like your age
and profession and what sort of things you enjoy; she can even read your
current mood. (Are you feeling lousy? Maybe it’s not the day for
“Apocalypse Now.”) A collaborative-filtering program, in contrast,
knows very little about you — only what you’ve bought at a Web site and
whether you rated it highly or not. But the computer has numbers on its
side. It may know only a little bit about you, but it also knows a
little bit about a huge number of other people. This lets it detect
patterns we often cannot see on our own. For example, Maes’s
music-recommendation system discovered that people who like classical
music also like the Beatles. It is an epiphany that perhaps make sense
when you think about it for a second, but it isn’t immediately obvious.

Photo

Soon after Maes’s work made its debut, online stores quickly understood
the value of having a recommendation system, and today most Web sites
selling entertainment products have one. Most of them use some variant
of collaborative filtering — like Amazon’s “Customers Who Bought This
Item Also Bought” function. Some setups ask you to actively rate
products, as Netflix does. But others also rely on passive information.
They keep track of your everyday behavior, looking for clues to your
preferences. (For example, many music-recommendation engines — like the
Genius feature on Apple’s iTunes, Microsoft’s Mixview music recommender
or the Audioscrobbler program at Last.fm — can register every time you
listen to a song on your computer or MP3 player.) And a few rare
services actually pay people to evaluate products; the Pandora
music-streaming service has 50 employees who listen to songs and tag
them with descriptors — “upbeat,” “minor key,” “prominent vocal
harmonies.”

Netflix came late to the party. The company opened for business in 1997,
but for the first three years it offered no recommendations. This wasn’t
such a big problem when Netflix stocked only 1,000 titles or so, because
customers could sift through those pretty quickly. But Netflix grew, and
today, it stocks more than 100,000 movies. “I think that once you get
beyond 1,000 choices, a recommendation system becomes critical,”
Hastings, the Netflix C.E.O., told me. “People have limited cognitive
time they want to spend on picking a movie.”

Cinematch was introduced in 2000, but the first version worked poorly —
“a mix of insightful and boneheaded recommendations,” according to
Hastings. His programmers slowly began improving the algorithms. They
could tell how much better they were getting by trying to replicate how
a customer rated movies in the past. They took the customer’s ratings
from, say, 2001, and used them to predict their ratings for 2002.
Because Netflix actually had those later ratings, it could discern what
a “perfect” prediction would look like. Soon, Cinematch reached the
point where it could tease out some fairly nuanced — and surprising —
connections. For example, it found that people who enjoy “The Patriot”
also tend to like “Pearl Harbor,” which you’d expect, since they’re both
history-war-action movies; but it also discovered that they like the
heartstring-tugging drama “Pay It Forward” and the sci-fi movie “I,
Robot.”

Cinematch has, in fact, become a video-store roboclerk: its suggestions
now drive a surprising 60 percent of Netflix’s rentals. It also often
steers a customer’s attention away from big-grossing hits toward
smaller, independent movies. Traditional video stores depend on hits;
just-out-of-the-theaters blockbusters account for 80 percent of what
they rent. At Netflix, by contrast, 70 percent of what it sends out is
from the backlist — older movies or small, independent ones. A good
recommendation system, in other words, does not merely help people find
new stuff. As Netflix has discovered, it also spurs them to consume more
stuff.

For Netflix, this is doubly important. Customers pay a flat monthly
rate, generally $16.99 (although cheaper plans are available), to check
out as many movies as they want. The problem with this business model is
that new members often have a couple of dozen movies in mind that they
want to see, but after that they’re not sure what to check out next, and
their requests slow. And a customer paying $17 a month for only one
movie every month or two is at risk of canceling his subscription; the
plan makes financial sense, from a user’s point of view, only if you
rent a lot of movies. (My wife and I once quit Netflix for precisely
this reason.) Every time Hastings increases the quality of Cinematch
even slightly, it keeps his customers active.

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But by 2006, Cinematch’s improving performance had plateaued. Netflix’s
programmers couldn’t go any further on their own. They suspected that
there was a big breakthrough out there; the science of recommendation
systems was booming, and computer scientists were publishing hundreds of
papers each year on the subject. At a staff meeting in the summer of
2006, Hastings suggested a radical idea: Why not have a public contest?
Netflix’s recommendation system was powered by the wisdom of crowds; now
it would tap the wisdom of crowds to get better too.

**AS HASTINGS HOPED**, the contest has galvanized nerds around the
world. The Top 10 list for the Netflix Prize currently includes a group
of programmers in Austria (who are at No. 2), a trained psychologist and
Web consultant in Britain who uses his teenage daughter to perform his
calculus (No. 9), a lone Ph.D. candidate in Boston who calls himself My
Brain and His Chain (a reference to a Ben Folds song; he’s at No. 6) and
Pragmatic Theory — two French-Canadian guys in Montreal (No. 3). Nearly
every team is working on the prize in its spare time. In October, when I
dropped by the house of Martin Chabbert, a 32-year-old member of the
Pragmatic Theory duo, it was only 8:30 at night, but we had to whisper:
his four children, including a 2-month-old baby, had just gone to bed
upstairs. In his small dining room, a laptop sat open next to children’s
books like “Les Robots: Au Service de L’homme” and a “Star Wars” picture
book in French.

“This is where I do everything,” Chabbert said. “After the kids are
asleep and I’ve packed the lunches for school, I come down at 9 in the
evening and work until 11 or 12. It was very exciting in the
beginning\!” He laughed. “It still is, but with the baby now, going to
bed at midnight is not a good idea.”

Pragmatic Theory formed last spring, when Chabbert’s longtime friend
Martin Piotte — a 43-year-old electrical and computer engineer — heard
about the Netflix Prize. Like many of the amateurs trying to win the $1
million, they had no relevant expertise. (“Absolutely no background in
statistics that was useful,” Piotte told me ruefully. “Two guys,
absolutely no clue.”) But they soon discovered that the Netflix
competition is a fairly collegial affair. The company hosts a discussion
board devoted to the prize, and competitors frequently help one another
out — discussing algorithms they’ve tried and publicly brainstorming new
ways to improve their work, sometimes even posting reams of computer
code for anyone to use. When someone makes a breakthrough, pretty soon
every other team is aware of it and starts using it, too. Piotte and
Chabbert soon learned the major mathematical tricks that had propelled
the leading teams into the Top 10.

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The first major breakthrough came less than a month into the
competition. A team named Simon Funk vaulted from nowhere into the No. 4
position, improving upon Cinematch by 3.88 percent in one fell swoop.
Its secret was a mathematical technique called singular value
decomposition. It isn’t new; mathematicians have used it for years to
make sense of prodigious chunks of information. But Netflix never
thought to try it on movies.

Singular value decomposition works by uncovering “factors” that Netflix
customers like or don’t like. Say, for example, that “Sleepless in
Seattle” has been rated by 200,000 Netflix users. In one sense, this is
just a huge list of numbers — user No. 452 gave it two stars; No. 985
gave it five stars; and so on. But you could also think of those ratings
as individual reactions to various aspects of the movie. “Sleepless in
Seattle” is a “chick flick,” a comedy, a star vehicle for Tom Hanks;
each customer is reacting to how much — or how little — he or she likes
“chick flicks,” comedies and Tom Hanks. Singular value decomposition
takes the mass of Netflix data — 17,770 movies, ratings by 480,189 users
— and automatically sorts the films. The programmers do not actively
tell the computer what to look for; they just run the algorithm until it
groups together movies that share qualities with predictive value.

Sometimes when you look at the clusters of movies, you can deduce the
connections. Chabbert showed me one list: at the top were “Sleepless in
Seattle,” “Steel Magnolias” and “Pretty Woman,” while at the bottom were
“Star Trek” movies. Clearly, the computer recognized some factor that
suggests that someone who likes the romantic aspect of “Pretty Woman”
will probably like “Sleepless in Seattle” and dislike “Star Trek.”
Chabbert showed me another cluster: this time DVD collections of the TV
show “Friends” all clustered at the top of the list, while action movies
like “Reindeer Games” and thrillers like “Hannibal” clustered at the
bottom. Most likely, the computer had selected for “comic” content here.
Other lists appear to group movies based on whether they lean strongly
to the ideological right or left.

As programmers extract more and more values, it becomes possible to draw
exceedingly sophisticated correlations among movies and hence to offer
incredibly nuanced recommendations. “We’re teasing out very subtle human
behaviors,” said Chris Volinsky, a scientist with AT\&T in New Jersey
who is one of the most successful Netflix contestants; his three-person
team held the No. 1 position for more than a year. His team relies, in
part, on singular value decomposition. “You can find things like ‘People
who like action movies, but only if there’s a lot of explosions, and not
if there’s a lot of blood. And maybe they don’t like profanity,’ ”
Volinsky told me when we spoke recently. “Or it’s like ‘I like action
movies, but not if they have Keanu Reeves and not if there’s a bus
involved.’ ”

**MOST OF THE LEADING TEAMS** competing for the Netflix Prize now use
singular value decomposition. Indeed, given how quickly word of new
breakthroughs spreads among the competitors, virtually every team in the
Top 10 makes use of similar mathematical ploys. The only thing that
separates their scores is how skillfully they tweak their algorithms.
The Netflix Prize has come to resemble a drag race in which everyone
drives the same car, with only tiny modifications to the fuel injection.
Yet those tweaks are crucial. Since the top teams are so close — there
is less than a tenth of a percent between each contender — even tiny
improvements can boost a team to the top of the charts.

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These days, the competitors spend much of their time thinking deeply
about the math and psychology behind recommendations. For example, the
teams are grappling with the problem that over time, people can change
how sternly or leniently they rate movies. Psychological studies show
that if you ask someone to rate a movie and then, a month later, ask him
to do so again, the rating varies by an average of 0.4 stars. “The
question is why,” Len Bertoni said to me. “Did you just remember it
differently? Did you see something in between? Did something change in
your life that made you rethink it?” Some teams deal with this by
programming their computers to gradually discount older ratings.

Another common problem is identifying overly punitive raters. If you’re
a really harsh critic and I’m a much more easygoing one, your two-star
rating may be equal to my four-star rating. To compensate, an algorithm
might try to detect when a Netflix customer tends to hand out only one-
or two-star ratings — a sign of a strict, pursed-lip customer — and
artificially boost his or her ratings by a half-star or so. Then there’s
the problem of movie raters who simply aren’t consistent. They might be
evenhanded most of the time, but if they log into Netflix when they’re
in a particularly bad mood, they might impulsively decide to rate a
couple of dozen movies harshly.

Photo

TV shows, which are hot commodities on Netflix, present yet another
perplexing issue. Customers respond to TV series much differently than
they do to movies. People who loved the first two seasons of “The Wire”
might start getting bored during the third but keep on watching for a
while, then stop abruptly. So when should Cinematch stop recommending
“The Wire”? When do you tell someone to give up on a TV show?

Interestingly, the Netflix Prize competitors do not know anything about
the demographics of the customers whose taste they’re trying to predict.
The teams sometimes argue on the discussion board about whether their
predictions would be better if they knew that customer No. 465 is, for
example, a 23-year-old woman in Arizona. Yet most of the leading teams
say that personal information is not very useful, because it’s too
crude. As one team pointed out to me, the fact that I’m a 40-year-old
West Village resident is not very predictive. There’s little reason to
think the other 40-year-old men on my block enjoy the same movies as I
do. In contrast, the Netflix data are much more rich in meaning. When I
tell Netflix that I think Woody Allen’s black comedy “Match Point”
deserves three stars but the Joss Whedon sci-fi film “Serenity” is a
five-star masterpiece, this reveals quite a lot about my taste. Indeed,
Reed Hastings told me that even though Net­flix has a good deal of
demographic information about its users, the company does not currently
use it much to generate movie recommendations; merely knowing who people
are, paradoxically, isn’t very predictive of their movie tastes.

As the teams have grown better at predicting human preferences, the more
incomprehensible their computer programs have become, even to their
creators. Each team has lined up a gantlet of scores of algorithms, each
one analyzing a slightly different correlation between movies and users.
The upshot is that while the teams are producing ever-more-accurate
recommendations, they cannot precisely explain how they’re doing this.
Chris Volinsky admits that his team’s program has become a black box,
its internal logic unknowable.

There’s a sort of unsettling, alien quality to their computers’ results.
When the teams examine the ways that singular value decomposition is
slotting movies into categories, sometimes it makes sense to them — as
when the computer highlights what appears to be some essence of
nerdiness in a bunch of sci-fi movies. But many categorizations are now
so obscure that they cannot see the reasoning behind them. Possibly the
algorithms are finding connections so deep and subconscious that
customers themselves wouldn’t even recognize them. At one point,
Chabbert showed me a list of movies that his algorithm had discovered
share some ineffable similarity; it includes a historical movie, “Joan
of Arc,” a wrestling video, “W.W.E.: SummerSlam 2004,” the comedy “It
Had to Be You” and a version of Charles Dickens’s “Bleak House.” For the
life of me, I can’t figure out what possible connection they have, but
Chabbert assures me that this singular value decomposition scored 4
percent higher than Cinematch — so it must be doing something right. As
Volinsky surmised, “They’re able to tease out all of these things that
we would never, ever think of ourselves.” The machine may be
understanding something about us that we do not understand ourselves.

Yet it’s clear that something is still missing. Volinsky’s momentum has
slowed down significantly, as everyone else’s has. There’s some X factor
in human judgment that the current bunch of algorithms isn’t capturing
when it comes to movies like “Napoleon Dynamite.” And the problem looms
large. Bertoni is currently at 8.8 percent; he says that a small group
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.”

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**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.

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“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)
