### ICANN: Netflix with Neural Networks and Restricted Boltzmann Machines

On Friday, Nichlosas Ampazis presented the second place submission on the netflix price challenge by the team the ensemble.

Unfortunately I can not find the paper online. It evaluates combinations of autoencoders, Restricted Boltzmann Machines and self organizing maps for collaborative filtering.

The approach is taken from Ruslan Salakhutdinvos paper Restricted Boltzmann Machines for Collaborative Filtering from ICML 2007.

Just a quick reminder: The netflix challenge was a benchmark problem in collaborative filtering. The basic problem in collaborative filtering is,

given some user ratings on some products, what would you expect people to think about products that they haven't rated.

This is the problem most recommendation systems are based on, like for example the Amazon recommendations - and of course the netflix recommendations, too.

The basic idea in using Restricted Boltzmann Machines to solve this problem is to learn the distribution of ratings. Products that have no ratings are simply treated as unknown. Then it is possible to infer or "fantasies" the missing data from the model, producing predictions for every user.

The winning entry (which by the way hat the same accuracy as the second but submitted 20 minutes earlier) uses a similar approach - and many others.

It can be found in the paper The BigChaos Solution to the Netflix Grand Prize.

Unfortunately I can not find the paper online. It evaluates combinations of autoencoders, Restricted Boltzmann Machines and self organizing maps for collaborative filtering.

The approach is taken from Ruslan Salakhutdinvos paper Restricted Boltzmann Machines for Collaborative Filtering from ICML 2007.

Just a quick reminder: The netflix challenge was a benchmark problem in collaborative filtering. The basic problem in collaborative filtering is,

given some user ratings on some products, what would you expect people to think about products that they haven't rated.

This is the problem most recommendation systems are based on, like for example the Amazon recommendations - and of course the netflix recommendations, too.

The basic idea in using Restricted Boltzmann Machines to solve this problem is to learn the distribution of ratings. Products that have no ratings are simply treated as unknown. Then it is possible to infer or "fantasies" the missing data from the model, producing predictions for every user.

The winning entry (which by the way hat the same accuracy as the second but submitted 20 minutes earlier) uses a similar approach - and many others.

It can be found in the paper The BigChaos Solution to the Netflix Grand Prize.

## Comments

## Post a Comment