Netflix Prize (The Lesson)

So a while back I posted a stumper: How Can You Use This (Ultrahard Edition).

The pictures were part of a larger set of 30. Here are two more examples:


Beijing Olympics Opening Ceremony


Lego Zombie Apocalypse

The only criterion for the images was that they be interesting in some way.

I showed the series of 30 images to my class and had them rate the images from 1 to 5. They could use any system they wanted.

I collected those ratings, then put them in a spreadsheet, selectively removing some of the ratings.


I gave the spreadsheet back to the students, then challenged them to work out the missing ratings.

Students could use any math trick they wanted (taking the mean of the known ratings is a good starting point; median and standard deviation are also recommended), but also psychology, media studies, or any other discipline, as long as their answers were justified.

This is all based on the Netflix Prize, a contest run by Netflix with a $1 million dollar prize attached. Winning the contest requires improving their existing “recommendation algorithm” by 10%. This article from the New York Times has a good summary of current progress, and why it is terribly hard to predict if someone will like Napoleon Dynamite.

Here’s one excerpt I find fascinating:

Interestingly, the Netflix Prize competitors do not know anything about the demographics of the customers whose taste they’re trying to predict. The teams sometimes argue on the discussion board about whether their predictions would be better if they knew that customer No. 465 is, for example, a 23-year-old woman in Arizona. Yet most of the leading teams say that personal information is not very useful, because it’s too crude. As one team pointed out to me, the fact that I’m a 40-year-old West Village resident is not very predictive. There’s little reason to think the other 40-year-old men on my block enjoy the same movies as I do. In contrast, the Netflix data are much more rich in meaning. When I tell Netflix that I think Woody Allen’s black comedy “Match Point” deserves three stars but the Joss Whedon sci-fi film “Serenity” is a five-star masterpiece, this reveals quite a lot about my taste. Indeed, Reed Hastings told me that even though Netflix has a good deal of demographic information about its users the company does not currently use it much to generate movie recommendations; merely knowing who people are, paradoxically, isn’t very predictive of their movie tastes.


One Response

  1. Cool way to illustrate the Netflix competition!

    Perhaps demographics are less than relevant – on the other hand, some people argue that the Netflix competition can’t be one won without additional data. Here’s my perspective:

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