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Close but no Cigar for Netflix Recommender System
Posted by
CmdrTaco
on Wed Nov 14, 2007 10:00 AM
from the fifty-grand-aint-bad dept.
from the fifty-grand-aint-bad dept.
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)"
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[+]
Science: Interest Still High In the Netflix Algorithm Competition 77 comments
circletimessquare brings us an update to the status of the million-dollar Netflix competition to develop a better algorithm for movie recommendations. We've discussed aspects of the competition since it started two years ago, but the New York Times has a lengthy overview of where it stands now.
"The Netflix competition is still going strong, with a vibrant, competitive roster of some 30,000 programmers around the globe hard at work trying to win the prize. The Times provides a look at some of the more obsessive searchers, such as Len Bertoni, a semi-retired computer scientist near Pittsburgh who logs 20 hours a week on the problem, oftentimes with the help of his children. There's also Martin Chabbert in Montreal: 'After the kids are asleep and I've packed the lunches for school, I come down at 9 in the evening and work until 11 or 12.' The article gets into the history of the search algorithm Netflix currently uses, and explores the hot commodity called 'singular value decomposition' that serves as the basis for most of the algorithms in competition."
[+]
Technology: Netflix Prize May Have Been Achieved 83 comments
MadAnalyst writes "The long-running $1,000,000 competition to improve on the Netflix Cinematch recommendation system by 10% (in terms of the RMSE) may have finally been won. Recent results show a 10.05% improvement from the team called BellKor's Pragmatic Chaos, a merger between some of the teams who were getting close to the contest's goal. We've discussed this competition in the past."
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1.57% (Score:2)
Re: (Score:2)
I would think they would not implement the AT&T team's solution given it did not reach the 10% mark, however AT&T has the lead in reaching that mark unless someone comes up with some quantum leap in design./p.
Re:1.57% (Score:5, Funny)
Parent
Re: (Score:2)
If they were at 50% accuracy and AT&T gave them 8.63%, if they implement that they are now at 58.63% accuracy. If they require a 10% increase then a new person will have to bring them up to 68.63% accuracy, much harder then the 60% AT&T was aiming for. Assuming that it becomes harder as you get closer to 100% accuracy.
Re: (Score:2)
-Dick Cheney
Re: (Score:2)
Not how it works (Score:5, Informative)
Parent
diminishing returns (Score:3, Interesting)
Don't meme me bro (Score:2)
I'd say... (Score:5, Insightful)
Re:I'd say... (Score:4, Funny)
Parent
Re: (Score:3, Interesting)
It's hard to say. On the one hand, it could be that the current system is good enough that improvements are minutely incremental, though 8+% is pretty good if you ask me. On the other, it may be that the system is so fraught with dependencies and/or the relationships are so variable that it's hard to make gigantic leaps in sophistication. Look at Amazon's recommendation system: pretty good overall but still makes some egregious errors. Add the tagging system to the mix and it's possible to lead the recommen
Re:I'd say... (Score:5, Funny)
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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.
Parent
Re:I'd say... (Score:5, Informative)
Egregious errors? It's downright useless unless you pretty much buy only one genre of book/music/whatever. Their system is heavily weighted towards whatever you most recently bought - and drops huge slabs of quasi related stuff into your request list at the slightest provocation.
I buy (among other things) serious works of culinary history, sociology, etc... Yet my reccomendation list is clogged with food porn (coffee table cookbooks) and the latest crap offerings from whichever TV chef is the current flavor of the moment. It also doesn't recognize the difference between editions - if you buy a hardback, it'll happily reccomend you buy the paperback. If you buy a frequently reprinted SF novel, it'll happily add each new printing/edition to your queue.
Parent
Re: (Score:3, Interesting)
AT&T Labs? (Score:2)
This is a great contest, considering they have to publicly release the solution.
Although what is AT&T doing working on this problem?
Re: (Score:2, Insightful)
Re: (Score:2)
Re:I'd say... (Score:5, Interesting)
If I liked Die Hard 4, for example, did I like it because of Bruce Willis, the "I'm a Mac" guy, the special effects, the plot, or some other reason that even I don't know?
Personally, I know that I have rated something like 900 movies on the netflix site and nearly all the recommendations are things I've no interest in or they simply say, "Sorry we have no recommendations for you at this time."
I would like to think that if they could ask me why I rated one movie a 4 and another a 1 then they might have more accurate recommendations. Even if they just had a drop down menu with something like, "I liked this movie because of a) the starts, b) the plot, c) the genre and so on" it would make recommendations a lot easier.
Parent
Re: (Score:3, Interesting)
Re:I'd say... (Score:4, Informative)
Parent
Re: (Score:2, Insightful)
This is the whole idea behind the field of "machine learning": inferring causes/relationship/
Re: (Score:3, Informative)
"Straightforward statistical linear models with a lot of data conditioning."
The Netflix programmers shouldn't necessarily get special recognition for using least-squares modeling, but feel free to pass on your praise to Gauss, Legendre, Galton, and Fisher.
What's amazing is how hard it is to improve drastically on these 150-year-old statistical techniques.
Re: (Score:2)
There's a whole lot of devil in those details, though.
It's a scam. (Score:2)
Moving target? (Score:5, Interesting)
Re: (Score:3, Interesting)
Trust (Score:2)
Re:Moving target? (Score:5, Informative)
Netflix is free to merge any improvements into their actual system in the meantime.
Parent
Bad title (Score:5, Funny)
Re: (Score:3, Informative)
Re: (Score:2)
Re:Bad title (Score:5, Funny)
Parent
Re: (Score:2)
Marijuana affects the memory.
Re: (Score:2)
no breaktrough - just blending (Score:5, Informative)
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).
hmmm .... (Score:5, Funny)
print "Not recommended";
}
That should improve the system by at least 20%
like trying to win the lottery (Score:2, Insightful)
I'm skeptical about these sorts of prizes. The X prize, Top Coder, Clay Institute Millennium Prizes-- if those were the only reasons to do something, few would. Seems pretty risky to do a lot of work for what amounts to a lottery ticket. So, who got 2nd place, and how well did they do? 1 group wins a paltry $50K and a little publicity and recognition, maybe even an endorsement or two, and the other 27000 plus get what? Nothing much. It's cool and fun to work on such problems, but people have bills to
The thrill of victory (Score:2)
And FYI, netflix doesn't get any "ideas" from anyone but the winner. You only have to submit your code if you win.
Re: (Score:2)
I think its good t
Re:like trying to win the lottery (Score:5, Insightful)
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).
Parent
Re: (Score:2)
Re:like trying to win the lottery (Score:5, Insightful)
There are two immediate reasons I can think of why anyone would bother competing:
1) To win money.
2) Because they enjoy the challenge of trying to solve an interesting problem.
I'm just a simple coder, and knew that I didn't have any realistic chance of winning money. But I still found it very satisfying to try to come up with a solution and send it in and see how I did. I don't regret spending hours of my own leisure time on the project.
That said, eventually I gave it up. It was very clear that I'm not smart enough to meet the challenge. I had my fun, and it was time to move on to the next project. In summary, I don't think it's safe to assume that everyone is in it for the money.
"Pardon my cynicism, but seems like contests like this are a way to get a lot of ideas and work for very little money."
I call it "brilliant". Netflix probably put some pricetag on what it would pay to get >10% improvement on their system. That pricetag is probably more than $1 million. That means profit!
Parent
partial credit? (Score:2)
Man, that's too easy... (Score:2)
some
if age 18 and male then hard porn and south park
if age > 18 and male and lives at home then any sci-fi movie (plus points if it's a sequel)
if age > 18 and female then any movie with Princess in the title
100% match up !
i thought this had already concluded (Score:2)
Here's an idea... (Score:4, Insightful)
For example, Netflix frequently recommends rated R movies to my family, but we have never rented a single R-rated movie and have no desire to do so. Moreover, every time we get a recommendation for an R-rated movie, we rate it "Not Interested." I've probably marked dozens of R-rated movies "Not Interested," but they continue to be recommended. (Either Netflix is trying to tell me to just give in and rent one already, or they really don't understand my family's movie preferences.)
A simple checkbox for "Do not recommend R-rated movies" would be all Netflix needs to substantially improve its accuracy for my family. I imagine Netflix could add checkboxes for similar criteria as well. In any case, I think a key point is giving more control over recommendations to the users themselves.
Buried gem (Score:3, Interesting)
This raises the question, should someone's age affect the denominator? People in or just out of college generally see their tastes evolve quickly, while people in retirement homes might take decades to get tired of something.
I also wonder if this decay factor applies to other fields. Not just books or music, but toothpaste or politicians. In the US, your representative is presumably re-elected before your opinion has time to change much; the president just as you're getting tired of him. It makes me wonder how Senators get re-elected at all.
Huh? (Score:2)
Sometimes throwing stuff away is good (Score:3, Interesting)