In this lecture, we'll be discussing the story of Netflix
and how their recommendation system is
worth a million dollars.
Through this example, we'll introduce
the method of clustering.
Netflix is an online DVD rental and streaming video service.
Customers can receive movie rentals by mail,
and they can also watch selected movies and TV shows online.
Netflix has more than 40 million subscribers worldwide
and has an annual revenue of $3.6 billion.
A key aspect of the company is being
able to offer customers accurate movie recommendations based
on a customer's own preferences and viewing history.
From 2006 through 2009, Netflix ran a contest asking the public
to submit algorithms to predict user ratings for movies.
This algorithm would be useful for Netflix
when making recommendations to users.
Netflix provided a training data set of about 100 million user
ratings and a test data set of about three million user
ratings.
They offered a grand prize of $1 million
to the team who could beat Netflix's current algorithm,
called Cinematch, by more than 10%
measured in terms of root mean squared error.
Netflix believed that their recommendation system
was so valuable that it was worth a million dollars
to improve.
The contest had a few rules.
One with that if the grand prize was not yet reached,
progress prizes of $50,000 per year
would be awarded for the best results
so far, as long as it was at least a 1% improvement
over the previous year.
Another rule was that teams must submit
their code and a description of the algorithm
to be awarded any prizes.
And lastly, if a team met the 10% improvement goal,
a last call would be issued, and 30 days
would remain for all teams to submit their best algorithm.
So what happened?
The contest went live on October 2, 2006.
By October 8, only six days later, a team
submitted an algorithm that beat Cinematch.
A week later, on October 15, there
were three teams already submitting
algorithms beating Cinematch.
One of these solutions beat Cinematch by more than 1%,
already qualifying for a progress prize.
The contest was hugely popular all over the world.
By June, 2007, over 20,000 teams had
registered from over 150 countries.
The 2007 progress prize went to a team called BellKor,
with an 8.43% improvement over Cinematch.
The following year, several teams from across the world
joined forces to improve the accuracy even further.
In 2008, the progress prize again went to team BellKor.
But this time, the team included members
from the team BigChaos in addition
to the original members of BellKor.
This was the last progress prize because another 1% improvement
would reach the grand prize goal of 10%.
On June 26, 2009, the team BellKor's Pragmatic Chaos,
composed of members from three different original teams,
submitted a 10.05% improvement over Cinematch,
signaling the last call for the contest.
Other teams had 30 days to submit algorithms
before the contest closed.
These 30 days were filled with intense competition and even
more progress.
But before revealing what happened,
let's investigate how we could try to predict user ratings.
In the next video, we'll discuss how recommendation systems
generally work.