Paper 1: Lessons from the Netflix Prize Challenge

Paper: Lessons from the Netflix Prize Challenge
Authors: Robert M. Bell and Yehuda Koren
Link: http://dl.acm.org/citation.cfm?id=1345448.1345465

In 2006, the movie renting website Netflix.com launched a competition in order to improve their movie recommendation engine. While no one achieved the target of a 10% improvement over their existing engine, a team out of AT&T labs as still able to come up with several significant improvements. The team was able to come up with four main improvements:

• A new method for computing nearest neighbor interpolation weights that better accounts for interactions among neighbors.
• A neighborhood-aware factorization method that improves standard factorization models by optimizing criteria more specific to targets of specific predictions.
• Integration of information about which movies a user rated into latent factor models for the ratings themselves.
• New regulation methods across a variety of models, including both neighborhood and latent factor models.

The article was very interesting overall. Rather than trying to come up with a completely new, more efficient algorithm, this paper focused on improving existing recommendation engines. It was nice to see a modern, real world example as well, rather than a strictly academic work.