Actually, this has already been pointed out in papers written on the subject. If you want to learn about the Netflix prize, read the papers from the teams that solved it, and don't waste your time on self-important bloggers who want attention.
akamaka, I can understand how my blog might come across as self-important - I know I can be over the top, but that's part of the idea (see the tag line of my blog).
The thing is, I'm writing for a general audience from the point of a general audience. I'm a designer not a programmer. And though I've read lots of articles in wired and on tech blogs about the netflix algorithm, I haven't heard much discussion about viable alternatives to the five star system. Yes, there are articles talking about how the rating system is faulty, and they talk about how to best "work around" the faults of the five star system. But I'm trying to brainstorm alternatives - scrap the system entirely and build an algorithm on something else. I'm sure the ideas are out there, I'm just indignant that as a general audience, we haven't heard about them yet. I'm getting great feedback already. Thanks greatly appreciated.
For such a little UI pattern, the five star system plays an enormous influence on how we see the internet, and the effects of it have a tangible impact offline as well - for example, restaurant traffic influenced by yelp reviews. I've heard a lot of people question how five star reviews influence the range of products that we're exposed to. It seems like an important question to ask. Look and now we've got a good brainstorm going.. Maybe think of my post as a challenge and request for a detailed article from someone who knows their stuff about rating systems and how they effect our everyday life. Cheers -
Hey, thanks for taking the time to reply. I realize that my comment probably comes across as being quite personal, but it more reflects my frustration at the lack of insight into the Netflix Prize that exists in the blogosphere.
Here are some prime examples of people making lots of noise without any data or science to back it up:
There's actually very few people who have made genuine contributions toward winning the Netflix prize, as can be see in the winning team's final publications. They only list about a half-dozen key papers as references.
Anyways, I apologize for directing my comments specifically at you. I totally agree with your basic point, and this is a problem I've been spending a lot of time thinking about myself. My personal view is that explicit rating systems should be totally eliminated, in favor of using data gathered automatically, without asking the user to provide a subjective rating. I don't either have any evidence to prove that's better, mind you. :)
It would be interesting to see a thumbs-up/thumbs-down rating combined with an optional tag classification system.
I don't think the average user will ever really tag anything. But as long as there's a sufficient quantity of interested users who would, I think you could get a lot closer to statistically guessing why people might like/dislike certain videos.
I like this idea a lot. I also think that once you have some tags, you could extrapolate which movies are likely to fall into those tag categories by seeing which movies people browsed on the site at the same time. You can get this information if you offer one additional step beyond flipping through thumbnails - such as watch a preview. Then with each browsing session you collect data you can use to map genres.
Actually, this has already been pointed out in papers written on the subject. If you want to learn about the Netflix prize, read the papers from the teams that solved it, and don't waste your time on self-important bloggers who want attention.