Stanford Data Mining Class Students Working on Netflix Prize

Anand Rajaraman's students at Stanford are working on the Netflix Prize in his data mining class.

Different student teams in my class adopted different approaches to the problem, using both published algorithms and novel ideas. Of these, the results from two of the teams illustrate a broader point. Team A came up with a very sophisticated algorithm using the Netflix data. Team B used a very simple algorithm, but they added in additional data beyond the Netflix set: information about movie genres from the Internet Movie Database (IMDB). Guess which team did better?

Team B got much better results, close to the best results on the Netflix leaderboard!! I'm really happy for them, and they're going to tune their algorithm and take a crack at the grand prize. But the bigger point is, adding more, independent data usually beats out designing ever-better algorithms to analyze an existing data set. I'm often suprised that many people in the business, and even in academia, don't realize this.

Did your homework ever give you a shot at $1 million dollars?

Comments

[Miner]

It's fairly common for data mining classes to use the Netflix contest for class projects these days. And it's relatively accessible since the state-of-the-art in data mining has really only progressed so far. It's not magic, yet.

First place in the recent progress prize was taken by a team from AT&T Research. Second place by a group which included Princeton undergraduates. These groups are really rather different (though they both come from New Jersey).

To a certain extent, the million-dollar prize is farther away than ever. Errors are decreasing logarithmically. It's now looking increasingly doubtful that incremental improvements will make it to the required 10%.