Slashdot Mirror
Close but no Cigar for Netflix Recommender System
Ponca City, We Love You writes "In October 2006, Netflix, the online movie rental service, announced that it would award $1 million to the first team to improve the accuracy of Netflix's movie recommendations by 10% based on personal preferences. Each contestant was given a set of data from which three million predictions were made about how certain users rated certain movies and Netflix compared that list with the actual ratings and generated a score for each team. More than 27,000 contestants from 161 countries submitted their entries and some got close, but not close enough. Today Netflix announced that it is awarding an annual progress prize of $50,000 to a group of researchers at AT&T Labs, who improved the current recommendation system by 8.43 percent but the $1 million grand prize is still up for grabs and a $50,000 progress prize will be awarded every year until the 10 percent goal is met. As part of the rules of the competition, the team was required to disclose their solution publicly. (pdf)"
Most noteworthy aspect of the winning entry is that their winning method works by combining 107 different types of prediction strategies.
They state that you can get pretty far by blending the 3-4 best strategies, but of course doing so would not have netted them the progress prize
It is kind of sad realization that there actually is no better method. Your best bet is to use brute force and attempt to find some weighting methodology that combines known methods. By the way this is a well known issue in protein structure prediction competitions, for many years now so called meta-servers (predictions work by merely combining other predictions) win all the time. The joke is that we now need meta-meta-servers, combine the results of combiners
Also a clarification on the progress prize: to get it you need to have at least 1% improvement over the previous result. Considering that there is only 1.57% to go there is room for only one more progress prize until it hits the Grand Prize (10% improvement over the original results).