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.

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Comments on “Winner Take All Outsourcing: Netflix Will Pay $1 Million To Whoever Improves Their Recommendation Engine”

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RantMax says:

No way!

“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.”

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.

Sanguine Dream says:

Yeah right...

And what is to stop them from taking in all the entries, inform all the entrants that none of them added the +!0% accuracy boost then a few months later they release a “new recommendation system”. And of course it will just happen to be modified (Netflix may just have the balls or ovaries to not even modify it) code from one or more of the entrants. And one more thing…Netflix will make sure to patent it before they release it in order to prevent law suits from the developer(s) behind this “new system”.

Nobody Special says:

other thoughts

1) If you word the contest correctly, you can take all the small incremental changes and simply not award the prize because no one person (or group) made the requisit amount of change.

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.

joh says:

I don’t know what they have in mind, but it should be easy to measure how accurate a recommendation system is. Just take a particular user, remove their rating for some movie, see what rating the engine predicts they would give that movie, and compare with what they actually gave it. Now repeat with a lot of different users/movies until you’re satisfied you’ve got the average accuracy.

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.

Craig (user link) says:

“Collaborative filtering” has a fairly substantial research base, especially in the computer science area. Many algorithms have been developed. The main problem, at least as I’ve come to understand it, is data sparsity: when you have 40,000 movies and 1.5 million customers, each customer has rated only like .01% of all films, making it _very_ hard to derive patterns among individuals that match well enough to make solid recommendations 90% of the time.

Dam says:

Here's My Improvement

Get the studios to release DVDs the same day as the theatrical release and then pay Netflix to run trailers on the site the way they do on TV. With all the hype surrounding most new movie releases, everyone’s recommendation can include the same day releases. The studios and Netflix will l make money, and the customers will be happy to see a hot new flick now instead of waiting – or going into a theater – yuck!

Netflix – just credit my account with $1MM and I’ll take it out in rentals for the next 50 years.

tim finin says:

Possible privacy problems

This dataset will be a great asset to researchers working on user modelling, machine learning and recommendation systems. There may be some privacy problems that surface, however. Given that you know I am a Netflx customer and a few unsuual moves that I’ve received from Netflix, can you pick out my profile? If so, it might reveal a few movies that I don’t want the everyone to know I rated highly, like Gidget Goes Hawaiian. Shades of Bork!

JG says:

Could be worth a shot

Why not? It doesn’t hurt Netflix to try. As noted, there is quite a bit of research in this field already, so it seems unlikely that anyone who is not already working on this will dream up a better solution, but who knows? And if you as a programmer think you would like to take a shot, more power to you! Just be careful…

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?

Lloyd Fassett says:

PR is cheap, curve jumping results require more

On the good side, this generates great PR and it does open up a lot of data for modelers to work with. Best of all, for Netflicks, is that they only pay for results and don’t have to guess at paying for people’s time and managing them. They get a lot of effort and only pay for results. It’s an important pay for performance model for the future.

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:

You can’t get something for nothing, except PR…

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