Recommender System - The Netflix Prize

The Netflix Prize

One of the key events that energized research in recommender systems was the Netflix prize. From 2006 to 2009, Netflix sponsored a competition, offering a grand prize of $1,000,000 to the team that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company's existing recommender system. This competition energized the search for new and more accurate algorithms. On 21 September 2009, the grand prize of US$1,000,000 was given to the BellKor's Pragmatic Chaos team.

The most accurate algorithm in 2007 used an ensemble method of 107 different algorithmic approaches, blended into a single prediction:

Predictive accuracy is substantially improved when blending multiple predictors. Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique. Consequently, our solution is an ensemble of many methods.

Many benefits accrued to the web due to the NetFlix project. Some teams have taken their technology and applied it to other markets, such as 4-Tell, Inc.'s NetFlix project-derived solution for ecommerce websites.

A second contest was planned, but was ultimately canceled in response to an ongoing lawsuit and concerns from the Federal Trade Commission.