… but only the little prize. The BellKor team focused on integrating predictions from many different methods. The base methods consist of:

- Nearest Neighbor Methods
- Matrix Factorization Methods (asymmetric and symmetric)
- Linear Regression on various feature spaces
- Restricted Boltzman Machines

The final predictor was an ensemble (as was reasonable to expect), although it’s a little bit more complicated than just a weighted average—it’s essentially a customized learning algorithm. Base approaches (1)-(3) seem like relatively well-known approaches (although I haven’t seen the asymmetric factorization variant before). RBMs are the new approach.

The writeup is pretty clear for more details.

The contestants are close to reaching the big prize, but the last 1.5% is probably at least as hard as what’s been done. A few new structurally different methods for making predictions may need to be discovered and added into the mixture. In other words, research may be required.

With all respect to the winner, the prize announcement raises a question. The differences in RMSE are small among the top submissions. One wonders whether their rank order would hold up across repeated test sets from the same distribution.