Winner Take All Outsourcing: Netflix Will Pay $1 Million To Whoever Improves Their Recommendation Engine
from the and-the-rest? dept
The concept of crowdsourcing has been popular lately, but not everything is easily sourced out to the crowd. Netflix is trying their hand at the game by throwing a little money into the pot. They're offering a million dollars to anyone who can improve the accuracy of their movie recommendation engine. Of course, there are some questions here. They claim they'll award the prize to whoever makes their system 10% or more accurate -- but it's not clear (at least from the NY Times article) how "accuracy" is determined. While some people do seem excited that Netflix has released a very large dataset for people to work on, part of this seems like an attempt to get a lot of free labor by making it a contest. It's true that contests like the X-Prize for space flight and the Grand Challenge for autonomous vehicles helped spur a lot of innovation -- but both were in general areas where the research efforts could easily lead to entire new industries. With a better recommendation system, it's basically just to help Netflix. And, to be honest, it just doesn't seem as exciting to help Netflix help you pick a better movie using collaborative filtering as it does to shoot a rocket into space. Still, this is an interesting trend, and you have to wonder if other companies will start experimenting with this "winner takes all" form of outsourcing -- or if people realize that all of the losers end up with nothing. Companies have plenty to gain if such programs work out -- but it seems like plenty of people will question whether it's worth building new tools for a company when they might not even get paid for it.Thank you for reading this Techdirt post. With so many things competing for everyone’s attention these days, we really appreciate you giving us your time. We work hard every day to put quality content out there for our community.
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No way!
You think?
As for accuracy, isn't it obvious: they'll only claim success if they have 10% boost of sales.
Of course that little thing called "customer budget" which may stop this from happening, even if the recommendation becomes extremely accurate.
Netflix's business model is based on quasi random actions in attempt to find what works by means of natural selection.
This is strangely familiar to what they are doing here.
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Yeah right...
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other thoughts
2) What really makes it different from X-Prize and Grand Challange is that both of those areas had people already working on it. In both of those contests, the winners would simply ofset their expenses. The real prize is being the winner. The money is more about generating excitement in the public and thus increasing the amount of bragging rights.
3) I suspect what Netflix really wants is something else. While it would be great to brag about how accurate the suggestions are.... the biggest benefit would be instead to use said engine to decide how many of each movie to buy. They waste money anytime the buy more copies of a movie then warrented by customer demand.
In any business dropping costs has obvious benefits. But in their business if they can cut cost of movies by say 5% it would be a huge gain for the bottom line.
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Holy cripes!
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And I don't see that there's anything sinister here. If you work on it and decide in the end your work is worth more than a shot at the prize, you don't have to give it to them.
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Here's My Improvement
Netflix - just credit my account with $1MM and I'll take it out in rentals for the next 50 years.
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Possible privacy problems
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Could be worth a shot
Of greater interest to me is the fact that Netflix is trying to improve at all. Wired recently ran an article about Netflix sponsoring the independent film movement, and they mentioned in the article how much "better" their recommendation search was compared to sites like Amazon. Constant improvement is great, but maybe its not as great as they claimed?
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PR is cheap, curve jumping results require more
On the bad side, the dataset is for collaborative filtering and they are looking for a way to find gold in their data. I sense enough good smart people have tried with collaborative filtering and the result for the prize is damn near impossible because anyone with depth in the practice would have been able to approach Netflix and Amazon and anyone else with their great algorithm anyway. And, if you did have that algorithm that could run on any collaborative filtering dataset, why would sell it for only $1million, when you could probably find 3 customers willing to pay that price?
This content and dataset format worked for finding gold in Canada because the problem posed and dataset were solvable within the given real world constraints. The spaceship prize worked because the result was demonstrable and the solution could come from anywhere.
This problem is different because to get 10% increase in accurracy, effectively jumping a curve, will require a new technology. This solution is looking for a new tool and that's not going to come out of the dataset. That will require money to be spent and different data to be collected, or even other data to be accessed than what is offered, which is where the contest is falling short.
What they need is the new new thing after Collaborative Filtering that explains this kind of phenomena: http://www.popvssoda.com/countystats/total-county.html
You can't get something for nothing, except PR...
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Re:
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I hate to say it, but..
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Lookie here: another $1 million bounty
http://crave.cnet.co.uk/desktops/0,39029426,49284049,00.htm
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RE: yeah right
Read about it here: http://www.greenspun.com/bboard/q-and-a-fetch-msg.tcl?msg_id=007w74
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