Thanks to Ravi, came to know about an old wired article on Netflix prize, where Gavin Potter used fundas from Behavioral economics to crack the problem

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.

Couldn’t a pure statistician have also observed the inertia in the ratings? Of course. But there are infinitely many biases, patterns, and anomalies to fish for. And in almost every case, the number-cruncher wouldn’t turn up anything. A psychologist, however, can suggest to the statisticians where to point their high-powered mathematical instruments. “It cuts out dead ends,” Potter says.