cainus · HackerTrans

il y a 18 ansdiscuss

I have no idea why (or even how, now that I think of it) you got downmodded (I put you back up)... I think in this day and age if a platform is going to be closed and non-free, it should have to justify those disadvantages. Where does it make up the slack?

cainus

il y a 18 ansdiscuss

I'd have to second this. The Pylons project doesn't seem to suffer from the NIH syndrome that the django project does, and because of that, they're free to choose best-of-breed tools for every scenario. I think the fact that turbogears 2.0 is going to be based on pylons is a great win for both projects. It really gives you the feeling that if TheGreatestTemplateEngineEver or TheTransparentestORMEver gets invented tomorrow, these guys will be the first to support it. That kind of unashamed blatant code thievery deserves some respect. ;)

cainus

il y a 18 ansdiscuss

While this is similar to Aptana's Jaxer project, Jaxer does server-side DOM stuff just as easily as client-side DOM stuff. I'm having a hard time imagining this framework does that. The only DOM stuff in the example is marked as clientside. With Jaxer, they actually have JQuery running serverside. I'm not sure how pyxer could even make use of JQuery clientside (or other frameworks).

cainus

il y a 18 ansdiscuss

It's probably just not that hot of a solution, believe it or not. The recommendation engine should be able to make much better associations between movies, without even being able to describe what those associations are. Looking at features like genre, director, actor etc, is like a spam filter looking for specific spammy words. As soon as the spammers start saying "p3n1s" instead or Eddy Murphy starts making family movies, it falls apart. Check out http://karmatics.com/docs/evolution-and-wisdom-of-crowds.htm... (scroll down to "3. Recommendation Systems" for the relevant part) for an explanation.

cainus

il y a 18 ansdiscuss

The article mentions tweaking the algorithm to take the timing of ratings into account. It gives the example that a person might rate two movies as 3/5, and a third they watch right after as a 4/5, because it was better than the previous two, but under normal(ized) circumstances that user would've given the 3rd movie a 3/5 too.

Soo.... it's an interesting notion, that there can be time-segment-based normalization of the data set. Team Bellkor/KorBell credit a big part of their gains to using the ordering of rankings, rather than the rankings themselves to test for similarity, so this guy's actually got a novel approach for normalizing the dataset. I really don't know how he can detect if he's dealing with the kind of person that is meticulous enough to make sure their new ratings take all their previous ratings into account, though, or if he's dealing with the kind of person that gives everything a rating from 4/5 to 5/5.. I wouldn't be surprised if he hits a wall because of this.

Really I think netflix would make a lot better strides towards their goals by improving their data collection technique. They could probably make great enhancements towards normalization just by saying "you gave this previous movie 3/5 stars. How do you rate this latest movie?" That way the data would be much more normalized. There are lots of possibilities for this sort of enhancement, so I'm not sure why they're only letting competitors look at the already collected dataset.

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