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).
Customers who bought the items in your shopping cart also bought:
Empress Charmeuse Silk Sheet Set - Queen - Ivory ~ $399.00
Black Leather Victorian Vintage Shaper Corset Boned Lace Up corset ~ $119.99
Orgazyme Clitoral Stimulation Gel, Topical, 0.8 oz ~ $20.79
Pampers Cruisers, Size 4, Economy Plus Pack, 140 Cruisers ~ $38.99
I don't think Amazon has much room to improve their recommendation technology.
Two reasons I can think of. One is the challenge. I like to code but I'm not great with coming up with projects to do myself. This kind of thing would be nice for that.
The other is the experience. If you get second in this, no, you won't win the prize. But you can bet that having that on your resume would make getting many jobs much easier. Amazon would like your skills. So would many other retailers.
Also, as a side note, it's not a lottery. There is a three prong legal test in the US to determine if something is a lottery. I think the three parts are you have to pay to get it, everyone has an equal chance of winning, and there is no skill involved. I'm not positive about the second part. This is free to enter and is based quite a bit on skill, so it's not like a lottery.
Don't exaggerate.
This isn't a way to get free work. It's a way to get very smart job candidates to find you. It's a recruiting tool. You don't honestly think that they will take the winning idea, pay the $1m, and then just say "bye" do you? They will offer that person a job if at all reasonable (if it's a team of 500 students, obviously they couldn't).
Comment forecast: Bits of genius surrounded by a sea of mediocrity.
Money can be exchanged for goods and services.