The Filter Bubble - Eli Pariser [12]
But when Amazon launched in 1995, everything changed. From the start, Amazon was a bookstore with personalization built in. By watching which books people bought and using the collaborative filtering methods pioneered at PARC, Amazon could make recommendations on the fly. (“Oh, you’re getting The Complete Dummy’s Guide to Fencing? How about adding a copy of Waking Up Blind: Lawsuits over Eye Injury?”) And by tracking which users bought what over time, Amazon could start to see which users’ preferences were similar. (“Other people who have similar tastes to yours bought this week’s new release, En Garde!”) The more people bought books from Amazon, the better the personalization got.
In 1997, Amazon had sold books to its first million customers. Six months later, it had served 2 million. And in 2001, it reported its first quarterly net profit—one of the first businesses to prove that there was serious money to be made online.
If Amazon wasn’t quite able to create the feeling of a local bookstore, its personalization code nonetheless worked quite well. Amazon executives are tight-lipped about just how much revenue it’s brought in, but they often point to the personalization engine as a key part of the company’s success.
At Amazon, the push for more user data is never-ending: When you read books on your Kindle, the data about which phrases you highlight, which pages you turn, and whether you read straight through or skip around are all fed back into Amazon’s servers and can be used to indicate what books you might like next. When you log in after a day reading Kindle e-books at the beach, Amazon is able to subtly customize its site to appeal to what you’ve read: If you’ve spent a lot of time with the latest James Patterson, but only glanced at that new diet guide, you might see more commercial thrillers and fewer health books.
Amazon users have gotten so used to personalization that the site now uses a reverse trick to make some additional cash. Publishers pay for placement in physical bookstores, but they can’t buy the opinions of the clerks. But as Lanier predicted, buying off algorithms is easy: Pay enough to Amazon, and your book can be promoted as if by an “objective” recommendation by Amazon’s software. For most customers, it’s impossible to tell which is which.
Amazon proved that relevance could lead to industry dominance. But it would take two Stanford graduate students to apply the principles of machine learning to the whole world of online information.
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As Jeff Bezos’s new company was getting off the ground, Larry Page and Sergey Brin, the founders of Google, were busy doing their doctoral research at Stanford. They were aware of Amazon’s success—in 1997, the dot-com bubble was in full swing, and Amazon, on paper at least, was worth billions. Page and Brin were math whizzes; Page, especially, was obsessed with AI. But they were interested in a different problem. Instead of using algorithms to figure out how to sell products more effectively, what if you could use them to sort through sites on the Web?
Page had come up with a novel approach, and with a geeky predilection for puns, he called it PageRank. Most Web search companies at the time sorted pages using keywords and were very poor at figuring out which page for a given word was the most relevant. In a 1997 paper, Brin and Page dryly pointed out that three of the four major search engines couldn’t find themselves. “We want our notion of ‘relevant’ to only include the very best documents,” they wrote, “since there may be tens of thousands of slightly relevant documents.”
Page had realized that packed into the linked structure