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Netflix Prize - One Team's Account (ieee.org)
87 points by mhb on May 21, 2009 | hide | past | web | favorite | 13 comments



A great overview on ensemble techniques in machine learning (what BellKor and many of the top competitors use):

http://abeautifulwww.com/EnsembleLearning.pdf


A fascinating account, I always have found discussions about the teams chasing this prize interesting.

Most interesting to me though was that it finally gave a clear description of how the contest worked. I've never looked into getting involved with the contest and the past articles I've read have rather glossed over the format and the way in which they run tests and judge entrants.



Interesting article. As of 5/13/09 "BellKor in BigChaos" team in the article have improved the algorithm by 9.71%. (http://www.netflixprize.com/leaderboard)


Is the contest over, or is it an ongoing one?


No one's won the Grand Prize yet, for which they're still offering $1M.

It'd be fascinating to know how much each percentage point increases sales.


Accuracy of Netflix's recommendation algorithm is the wrong place to focus if you'd want to increase sales:

1) It is only perceptible after someone is a customer.

2) It is not strongly perceptible (people suck at precise measurements -- that is why we have computers tell us when the root mean squared error is 9.63% instead of just looking at our slice of 4 movie recommendations and saying "HOT DAMN that is better than last week -- you gave Saving Private Ryan 3.5 stars -- a strong improvement over your last rating of it at 3, which didn't quite represent my interest.")

3) It only affects a portion of the customer base. Netflix is a service for delivering movies, not for rating them. The feature is doubtless useful to many and of intense interest to a few, but there are equally doubtlessly many Netflix customers who don't even know it exists.

Now, there might be some bonus for having the perception of having better accuracy (or, for that matter, geek cred) from having the Netflix prize... but that wouldn't be tied to the objective reality of whether the algorithm actually improved or not.

It is likely that A/B testing on the call to action in the signup button would moev the needle a lot more, for a lot less work. (Please don't stone me too harshly.)


Netflix has said that they are more interested in retaining customers than acquiring new ones. People sign up, watch the stuff they can think of, then quit. If they reliably use the recommendation algorithm, they will stay profitable much longer.

If this wasn't their worry, the recommendation algorithm is counterproductive-- by giving consumers more movies to like they watch Netflix increases its own postage costs, etc.


RMSE also penalizes for a bad prediction of your 1000th-from-the-top best prediction as much as your top prediction.

Amazon's algorithm is actually quite simple (at least as much as can be extrapolated from the outside, and confirmed by interviews with the people that wrote it), but they do a lot of work to place recommendations in context. I think that's one of the critical places to focus on when looking at raw sales boosts.


Yep, context is key. I recall reading that Amazon found placing the "This is recommended because you bought book X" actually reassured their customers a lot. It seems like people prefer recommendation algorithms they can understand, rather than complex ones that may be more accurate.


Absolutely; makes me think back to this item (http://news.ycombinator.com/item?id=590276) - the power of 'because'; whatever follows because only has to sound plausible in the blink of an eye that will be spent assessing whether the user trusts/pays attention to what amazon is telling him/her.


Aside from this, even if the teams are able to improve the algorithm tremendously, Netflix is looking for the wrong solution.

The ability to predict that, say, movie ABC would score a 3 from customer X really isn't very helpful for anything.

As a Netflix customer, I want them to suggest things that I'm likely to rate 5, and perhaps to warn me away from things that I'd consider a 1 or 2. I want to ignore 3 and 4 movies, so it doesn't matter if the algorithm can predict them well. It also doesn't matter alot if it is able to ferret out every possible 5; as long as it gets a substantial portion, everything is great.

I played a bit with this contest when it first started, along with a friend. In just one night, I was able to beat Netflix's own algorithm by enough to get us onto the leaderboard (temporarily). And the algorithm I used was nothing fancy, just correct scaling and normalizing using means and standard deviations: high school math. Unfortunately, neither of us knew anything about algorithms for collaborative filtering, so we quickly reached a plateau.


As patio points out, this won't really affect sales because it doesn't measurably alter the top line (revenue).

What it does do (assuming) is modify that bottom line (profit) in a number of ways:

1. Given the same $1M expenditure on product development, nets better results. 2. Free advertising (reducing expenses of that department) with all this indirect marketing.

Additionally, a better algorithm helps them to secure their position in the market, which is really more of a strategic move; hard to measure.




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