In October 2006, Netflix announced it would give a cool seven figures to whoever created a movie-recommending algorithm 10 percent better than its own. Within two weeks, the DVD rental company had received 169 submissions, including three that were slightly superior to Cinematch, Netflix's recommendation software..
..Potter likes to use what psychologists know about human behavior. "The fact that these ratings were made by humans seems to me to be an important piece of information that should be and needs to be used," he says. Potter has great respect for the technical prowess of BellKor he is, after all, still behind the team in the rankings but he thinks the computer science community studying this problem suffers from a bad case of groupthink. He refers to the psychological model underlying their mathematical approach as "crude.."
..A deeper part of Potter's strategy is based on the work of Amos Tversky and Nobel Prize winner Daniel Kahneman, pioneers of the science now called behavioral economics. This new field incorporates into traditional economics those features of human life that are lost when you think of a person as a rational machine, or as a list of numbers representing cinematic taste. One such phenomenon is the anchoring effect, a problem endemic to any numerical rating scheme. If a customer watches three movies in a row that merit four stars say, the Star Wars trilogy and then sees one that's a bit better say, Blade Runner they'll likely give the last movie five stars. But if they started the week with one-star stinkers like the Star Wars prequels, Blade Runner might get only a 4 or even a 3. Anchoring suggests that rating systems need to take account of inertia a user who has recently given a lot of above-average ratings is likely to continue to do so. Potter finds precisely this phenomenon in the Netflix data; and by being aware of it, he's able to account for its biasing effects and thus more accurately pin down users' true tastes.
