Is it possible to love The Usual Suspects and hate Se7en? Glance
at keywords and genres, and they look nearly identical. Both are Crime
thrillers, starring Kevin Spacey, flagged for violence. Of course, one
is a witty cons and scams story with a surprise twist, and the other is
a disturbing, gory morality tale about a serial killer. But you’ve got
to look inside the movies to see that.
Taste in movies is subtle, complex and unique. Yet the usual way of cataloging movies, by titles, people, and genres, flattens all this – as if you’d like a movie just because it’s Sci-Fi or stars Vince Vaughn. That’s why our team of movie and TV experts created the Movie Genome, an ambitious, ongoing project with the Jinni community to map more aspects of movies, shows, and semi-professional videos than ever before – so that all different viewers can match their personal tastes and moods, and find what they really want to watch next. The Movie Genome powers the search, recommendations, Movie personality and taste matches on Jinni.
Inside, the Genome is divided in two: Experience – the mood and tone of the content – and Story – plot elements (One man army, Battle of the sexes), structures (Nonlinear, Story-within-a-story), flags (Violence, Nudity) and more. The Genome also includes many external aspects like awards.
The starting point of the Movie Genome is manual tagging by our team of film professionals. Each title has around fifty genes, among thousands of possibilities. Then, using advanced machine-learning technology, Jinni’s system learns from the manual tagging to begin automated tagging. This creates a level of consistency that creative human taggers can’t reach – especially important for similarity matches and recommendations, which won’t work unless you compare apples to apples and unhappy families to unhappy families as often as possible. Users who vote on genes, as well as the Jinni team, constantly check and improve the machine tagging.
Jinni assigns a set of genes to each user, drawn from aspects of the content you like. These genes, along with some other parameters, are constantly adjusted as we learn from your ratings, reviews, and other actions on Jinni. Each user also gets a Movie personality name (Drama Addict, Escape Artist) that describes general preferences. Tastes are unique, though, and two users with the same personality name will not have exactly the same genes and full Movie personality.
We think the best recommendations use man and machine. A machine can deeply analyze the type of content you like to learn about your unique taste. People can share their personal favorites and opinions about what they’ve seen (in a way no machine can do as yet). Jinni isn’t a social network, it’s a service meant to fit how people enjoy movies and shows – and conversation is part of that.
Jinni recommends by comparing your Movie personality and the genes of all the titles in our catalogue, figuring in your preferences (movies or TV, in theaters or online) and some other filters. Simplistically, if you have the genes for Gloomy Love triangles, we’ll recommend titles with those dominant genes. My Recommendations integrates recommendations from Jinni, your Movie circle, your Neighbors (people Jinni identifies as having taste similar to yours), and Groups.
Our vision of the Wiki is a constantly evolving dictionary of genes that describes multiple aspects of video, where users can shape the Movie Genome Project by discussing and editing definitions, voting on genes, and more.