If transpromo needs awesome recomendation engines, the answer might be movies+a contest. Or call Gravity . . and lowering SAG

Imagine a recommendation engine for education. as in "if you liked reading this, then consider reading this." Or if you enjoyed working out this problem, consider working out this one."

In general a recommendation engine takes the form of "If you liked doing X, consider doing Y." That might turn out to be the operational definition of being a coach or a mentor.

Netflix Competitors Learn the Power of Teamwork -
from NYTimes.com July 28, 2009:
"A contest set up by Netflix, which offered a $1 million prize to anyone who could significantly improve its movie recommendation system, ended on Sunday with two teams in a virtual dead heat, and no winner to be declared until September.

But the contest, which began in October 2006, has already produced an impressive legacy. It has shaped careers, spawned at least one start-up company and inspired research papers. It has also changed conventional wisdom about the best way to build the automated systems that increasingly help people make online choices about movies, books, clothing, restaurants, news and other goods and services.

These so-called recommendation engines are computing models that predict what a person might enjoy based on statistical scoring of that person’s stated preferences, past consumption patterns and similar choices made by many others — all made possible by the ease of data collection and tracking on the Web.

“The Netflix prize contest will be looked at for years by people studying how to do predictive modeling,” said Chris Volinsky, a scientist at AT&T Research and a leader of one of the two highest-ranked teams in the competition.

. . .

Software recommendation systems, Mr. Mackey said, will increasingly become common tools to help people find useful information and products amid the explosion of information and offerings competing for their attention on the Web. “A lot of these techniques will propagate across the Internet,” he predicted.

That is certainly the hope of Domonkos Tikk, a Hungarian computer scientist and a member of the Ensemble. Mr. Tikk, 39, and three younger colleagues started working on the contest shortly after it began, and in 2007 they teamed up with the Princeton group. “When we entered the Netflix competition, we had no experience in collaborative filtering,” Mr. Tikk said.

Yet based on what they learned, Mr. Tikk and his colleagues founded a start-up, Gravity, which is developing recommendation systems for commercial clients, including e-commerce Web sites and a European cellphone company.

The best and brightest from many disciplines joined together. The didn't deliver a paper or an approach or information. They solved a well measured problem at a total cost of $1,000,000. There was no additional overhead, health care or pension benefits.

That's just one way to get SAG down,down,down, fast, fast, fast.