That's all well and good, but now there's a very practical problem with is leading to a new way of thinking about this. Basically, the problem is that you've been listening to music through Pandora, or renting movies via Netflix, and you'd like to know what other music or movies would appeal to you.
These online content sources have tons of movies and music, but the problem is how to classify it so that you can identify which ones might appeal to audience members with certain tastes. Netflix, for example, asks you to rate each movie you rent, and over time, it builds up a database of what movies you've liked.
But there's still the similarity problem: If your favorite movies were Ishtar and Gigli, how to you decide which other movies are similar?
One promising answer is the movie genome. Basically, the idea is to identify a slew of properties that a movie can have, like plot, cast, awards, box office success, etc. Comparisons of movies on the basis of their genomes are likely to good matches and non-matches.
Now, you might think "Isn't this just a fancy way of comparing all these properties? Calling them a genome doesn't really change anything."
The answer is "Well, yes." It is just a fancy way of comparing these things. But treating all these properties as genes accomplishes two things:
- It makes the classification of movies more systematic, and
- It makes it possible to use some elaborate algorithms for doing the comparisons, finding near-matches, etc.
Or not.
1 comments:
Interesting thoughts on the complexity of recommendations. Jinni (http://www.jinni.com) recently opened in private beta and offers search and recommendations from the Movie Genome. Have a look and see what you think!
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