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)"

Re:I'd say...

[wattrlz]

Perhaps they should look at whatever chooses the slashdot page-bottom quotes for inspiration: Mosher's Law of Software Engineering: Don't worry if it doesn't work right. If everything did, you'd be out of a job.

Bad title

[markov_chain]

The prize was clearly a million dollars, not a cigar! I guess the editors don't even read the summary.

hmmm ....

[Average_Joe_Sixpack]

if ($director eq "Michael Bay") {
        print "Not recommended";
}

That should improve the system by at least 20%

Re:1.57%

[stoolpigeon]

Right - but the AT&T method is public - so if you can get that the rest of the way to 10%..... I guess step 3 is profit.

Re:I'd say...

[Anne_Nonymous]

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I don't think Amazon has much room to improve their recommendation technology.

Re:Bad title

[shadow349]

Money can be exchanged for goods and services.