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Netflix Prize May Have Been Achieved
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."
C'mon, the Netflix prize isn't THAT well known. At least you could have given some basic info about it.
Knowledge is power. Knowledge shared is power lost.
Let's see, $1,000,000 split 7 ways gives us $142,857.14 each. Let's say taxes take half, now you are down to only $71,428.57 each. Unless one of them kills all of their partners like in The Dark Knight that ain't much of a prize.
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Assuming no-one turns up a better score in the next 30 days, do the team members who work for Yahoo and AT&T get to keep their share of the prize money?
The real "Libtards" are the Libertarians!
Well done Bellkor.
But now the real race begins.
Now that the 10% barrier has been reached, people have 30 days to submit their final results. At the end of the 30 days, whoever has the best result wins.
This is going to be a great month!
by simply ignoring data from anyone who ever rented SuperBabies: Baby Geniuses 2, Gigli, From Justin to Kelly, Disaster Movie, any movie by Uwe Boll and any movie starring Paris Hilton
suddenly, everything made sense
intellectual property law is philosophically incoherent. it is your moral duty to ignore it or sabotage it
Now will somebody please fix that goddamned Silverlight player?
for simple intellectual satisfaction, like a giant puzzle or a game of chess
money is not the motivation for everything in this world
intellectual property law is philosophically incoherent. it is your moral duty to ignore it or sabotage it
I published a paper using Netflix data. (Yeah, that group.)
It's certainly cool that they beat the 10% improvement, and it's a hell of a deal for Netflix, since it would have cost them more than a prize money paid out to hire the researchers, the interesting thing is whether or not this really advances the the field of recommendation systems.
The initial work definitely did, but I wonder how much of the quest for the 10% threshold moved the science, as opposed to just tweaking an application. Recommender systems still don't bring up rare items, and they still have problems with diversity. None of the Netflix Prize work address any of these problems.
Still, I look forward to their paper.
batman even has a better sense of humor than you ;-)
intellectual property law is philosophically incoherent. it is your moral duty to ignore it or sabotage it
That actually makes a lot of sense... Remove the wildcards and you'll be able to get a much more accurate result for everyone else. You might suffer a bit when you're reccomending to people who like movies that are absolutely terrible, but you'd make up for it my not factoring that into the equation at all. This is, of course, assuming that only a few people actually watched those movies.
Who listens to these sort of things anyway?
I want to delete my account but Slashdot doesn't allow it.
from the excellent nyt article about the competition in november:
http://science.slashdot.org/article.pl?sid=08/11/22/0526216
it isn't bad movies that are the problem, taste in bad movies can still be uniform
the real problem is extremely controversial movies, most notably Napoleon Dynamite
http://www.imdb.com/title/tt0374900/
not controversial in terms of dealing with abortion or gun control, but controversial in terms of some people really found the movie totally stupid, while some people really found the movie to be really funny
movies like napolean dynamite are genre edge conditions, and people who apparently agree on everything else about movies in general encounter movies like this one and suddenly dramatically differ on their opinion of it, in completely unpredictable ways
intellectual property law is philosophically incoherent. it is your moral duty to ignore it or sabotage it
by simply ignoring data from anyone who ever rented SuperBabies: Baby Geniuses 2, Gigli, From Justin to Kelly, Disaster Movie, any movie by Uwe Boll and any movie starring Paris Hilton
Hey, I (along with the rest of my frat, our school hockey team, and most of the town) was in a movie starring Paris Hilton, you insensitive clod!
You can generalize that to any movie with an action hero, and a baby. Any movie with a pseudo-star (Hilton, Spears, Madonna, etc.). And any movie with Uwe Boll or similar people.
On a more serious note: I think the best way to improve recommendations, is to first relate the IMDB rating to the IQ of the rater. I found that more intelligent people do not like movies with a simple plot, because it bores them, and less intelligent people do not like movies with a complex, subtle plot, because they don't get it. You can further separate this into the EQ and the IQ, which will considerably improve the experience for emotional people (like most women).
I found that the best way to quickly and realistically do that, is to check the length of the sentences and the percentage of the questions in the comments of that user.
This will give you a 3D space with a rating, a EQ and a IQ axis.
Now apply the values of the user that wants to get recommendations as coordinates, and order by the distance from that point.
Then apply the traditional recommendation system with a blending factor.
I bet I could code that in less than an hour in Haskell.
P.S.: If you want to patent this, mind you that I will seriously kick your ass for doing so. I do not know any laws when it comes to giving credit. You can probably sue me afterwards, but it won't fix what I did to you. ^^ (Be fair to me, and I will be the nicest man you ever met.)
Any sufficiently advanced intelligence is indistinguishable from stupidity.
this is my career, doing behavioral data analysis.
I was really excited when they announced the project, and began tinkering with it. Unfortunately, they stripped so much data out of the exercise that it became an academic statistical exercise, rather than an insightful behavioral modeling exercise. It rewarded an approach where the training population was continually wildly segmented, with different model parameters on each segment.
I wish they had opened up more data, there could have been lots of cool stuff in there. I'm not saying it was bad or against it, just not *my* cup of tea. I rather prefer statistically simple models driven off unique customer segments.
So... What does this mean in real-world analysis? What does the score represent? Since the score shown seems to be smaller-is-better, does this mean that 85+% of the movies recommended won't be attractive to the target, and less than 15% would be found interesting?
That doesn't seem very accurate...
Hey, I (along with the rest of my frat, our school hockey team, and most of the town) was in Paris Hilton, you insensitive clod!
Fixed
Kind of what I was implying...
You're being elitist and silly. Perhaps you could do some research, but I have seen no evidence that interest in simple vs. complex plots has anything to do with intelligence. Certainly the type of plot one likes in a movie is something reasonable to consider. But assuming a relationship to IQ or EQ from that is silly.
Recent results show a 10.05% improvement
How many library of congresses is that?
by simply ignoring data from anyone who ever rented SuperBabies: Baby Geniuses 2, Gigli, From Justin to Kelly, Disaster Movie, any movie by Uwe Boll and any movie starring Paris Hilton
suddenly, everything made sense
Ok, From Justin to Kelly wasn't really that bad. Now, Ishtar...and Battlefield Earth; those were baaaad.
Does anyone find Netflix recommendations any good anyway? I used http://criticker.com/ for quite a while and was very happy about the recommended stuff. Recently switched to http://filmaster.com/ (which is a free service) and it's equally good, even though both probably use a pretty simple algorithm compared to Nextflix.
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