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Yet another psychological problem mathematicians too eagerly claimed as their own. Why not try to find patterns about the movies which appeal to a certain indiv

by andreyf 19y ago

Yet another psychological problem mathematicians too eagerly claimed as their own. Why not try to find patterns about the movies which appeal to a certain individual? Is it against the rules to use outside data (actors, directors, etc)?

andreyf 19y ago
From the Netflix prize FAQ: Why not provide other data about the movies, like genres, directors, or actors? We know others do. Again, Cinematch doesn’t currently use any of this data. Use it if you want. That seems like an easy target - if I've 5-starred every movie with Kevin Spacey, I probably will like anything with Kevin Spacey. Why not mine blogs and reviews, trying to find themes which are appealing to individuals?
- pchristensen 19y ago
  I think the limiting factor is the amount of computation required. When you take the number of variables (actors, directors, themes, box office gross, etc) x the number of values each of them have (dozens to thousands) x the number of users, that's a lot of crunching to do. Perhaps do some pre-crunching to find actors that have a correlation like that and then just check those. Apply the "Kevin Spacey index", if you will.
- cainus 19y ago
  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.html http://karmatics.com/docs/evolution-and-wisdom-of-crowds.htm... (scroll down to "3. Recommendation Systems" for the relevant part) for an explanation.
  - toffer 19y ago
    Great link. Thanks for posting that.

by andreyf 19y ago

andreyf 19y ago

From the Netflix prize FAQ: Why not provide other data about the movies, like genres, directors, or actors? We know others do. Again, Cinematch doesn’t currently use any of this data. Use it if you want. That seems like an easy target - if I've 5-starred every movie with Kevin Spacey, I probably will like anything with Kevin Spacey. Why not mine blogs and reviews, trying to find themes which are appealing to individuals?

pchristensen 19y ago
I think the limiting factor is the amount of computation required. When you take the number of variables (actors, directors, themes, box office gross, etc) x the number of values each of them have (dozens to thousands) x the number of users, that's a lot of crunching to do. Perhaps do some pre-crunching to find actors that have a correlation like that and then just check those. Apply the "Kevin Spacey index", if you will.
cainus 19y ago
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.html http://karmatics.com/docs/evolution-and-wisdom-of-crowds.htm... (scroll down to "3. Recommendation Systems" for the relevant part) for an explanation.
- toffer 19y ago
  Great link. Thanks for posting that.