If You Liked This, Sure to Love That
nytimes.com
If You Liked This, Sure to Love That
1–10 of 27 posts
Re: If You Liked This, Sure to Love That
#2That's hilarious. The list of movies that are hard to classify reads like a list of my favorites...
[Like] “Napoleon Dynamite” — culturally or politically polarizing and hard to classify, including “I Heart Huckabees,” “Lost in Translation,” “Fahrenheit 9/11,” “The Life Aquatic With Steve Zissou,” “Kill Bill: Volume 1” and “Sideways.”
Re: If You Liked This, Sure to Love That
#3I worked on the Netflix Challenge last year but did not come far and gave up after submitting a few mediocre results. Actually, I was proud of myself for my result did not explode but was within the range of the Cinematch's algorithm. The experience has given me a lot of respect for companies that are developing recommendation algorithms.
There should be more great competition like this. At this point, I'd say the benefits coming from all the research and development by the Netflix Challenge community as well as the experience obtained by many hobbyists like myself as the result of this competition has already exceeded the $1 million winning prize.
Re: If You Liked This, Sure to Love That
#4If those teams got together, hacked up a program that ran 2 or more independant systems (entries) at the same time and averaged the results, they'd probably get to 10 percent.
Re: If You Liked This, Sure to Love That
#5If those teams got together, hacked up a program that ran 2 or more independant systems (entries) at the same time and averaged the results, they'd probably get to 10 percent.
I prefer the capitalist approach.
Re: If You Liked This, Sure to Love That
#6If those teams got together, hacked up a program that ran 2 or more independant systems (entries) at the same time and averaged the results, they'd probably get to 10 percent.
There are lots of teams on the leaderboard that are actually metateams. My favorite is "When Gravity and Dinosaurs Unite."
http://www.netflixprize.com/leaderboard
They actually do one better than your suggestion, which is that they use machine learning to figure out how to weight one team's results vs. the other.
Re: If You Liked This, Sure to Love That
#7That's hilarious. The list of movies that are hard to classify reads like a list of my favorites... [Like] “Napoleon Dynamite” — culturally or politically polarizing and hard to classify, including “I Heart Huckabees,” “Lost in Translation,” “Fahrenheit 9/11,” “The Life Aquatic With Steve Zissou,” “Kill Bill: Volume 1” and “Sideways.”
I would classify those all as Indie mainstream, stuff that is popular with the scenester, psuedo-intellectual, pretentious crowd.
Re: If You Liked This, Sure to Love That
#8If those teams got together, hacked up a program that ran 2 or more independant systems (entries) at the same time and averaged the results, they'd probably get to 10 percent.
Yeah, what he said:
http://www.onderzoekinformatie.nl/en/oi/nod/onderzoek/OND127...
Re: If You Liked This, Sure to Love That
#9I worked on the Netflix Challenge last year but did not come far and gave up after submitting a few mediocre results. Actually, I was proud of myself for my result did not explode but was within the range of the Cinematch's algorithm. The experience has given me a lot of respect for companies that are developing recommendation algorithms. There should be more great competition like this. At this point, I'd say the be…
I saw an informal study on the economics of prizes. Apparently, the monetary value of the effort that is put into solving the, the media publicity, etc. added together exceeds the value of the prize itself by anywhere between a factor of 10 and 50, depending on the prize.
Re: If You Liked This, Sure to Love That
#10I'm a fan of this old post (probably linked from Hacker News, actually, but I can't really remember)...
http://whimsley.typepad.com/whimsley/2007/07/the-limitations...
...that asks exactly how much better the experience will be for a particular customer if all this research results in a recommendation engine that's really 10% better than Cinematch. Undoubtedly the marketing value (to Netflix) of the challenge is incredible, and I don't question the reasonableness of their desire to eke satisfaction out of every potentially satisfied customer; but surely there's somewhere else in their business they could more easily improve their margin and their customers' experience.
I, personally, use Netflix a bunch, and love the service. Furthermore, I love the fact that they sponsored this contest and provided a large research database to support it. I guess I'm just always a bit disappointed by breathless mainstream press coverage that doesn't discuss these other meta-contest questions. That and I wish I could somehow get Netflix to send me season 4 of Lost before season 5 starts.