Chapter One

Chapter One


Who's Doing Your Thinking for You?

Recommendations make life a lot easier. Want to know what movie to rent? The traditional way was to ask a friend or to see whether reviewers gave it a thumbs-up.

Nowadays people are looking for Internet guidance drawn from the behavior of the masses. Some of these "preference engines" are simple lists of what's most popular. The New York Times lists the "most emailed articles." iTunes lists the top downloaded songs. Del.icio.us lists the most popular Internet bookmarks. These simple filters often let surfers zero in on the greatest hits.

Some recommendation software goes a step further and tries to tell you what people like you enjoyed. Amazon.com tells you that people who bought The Da Vinci Code also bought Holy Blood, Holy Grail. Netflix gives you recommendations that are contingent on the movies that you yourself have recommended in the past. This is truly "collaborative filtering," because your ratings of movies help Netflix make better recommendations to others and their ratings help Netflix make better recommendations to you. The Internet is a perfect vehicle for this service because it's really cheap for an Internet retailer to keep track of customer behavior and to automatically aggregate, analyze, and display this information for subsequent customers.

Of course, these algorithms aren't perfect. A bachelor buying a one-time gift for a baby could, for example, trigger the program into recommending more baby products in the future. Wal-Mart had to apologize when people who searched for Martin Luther King: I Have a Dream were told they might also appreciate a Planet