Jinni, A Film and Television Recommendation Engine Launches Public Beta

Jinni has been around for many months as a private beta (you needed to request an invite). Yesterday, the beta became available to all. Jinni is a resource that might be very useful in a library and for library users. It’s both useful and fun (especially if you’re a tv and/or film fan).

+ Register (free) to receive personal recommendation. However, you don’t have to be a registered user to use the database.

+ Search by entering plots, moods, titles, people, etc.

+ Limit your search to: All, Movies, TV, Shorts, Free Online (Content that can be viewed online).

+ You can focus your results (left side of page) by category. Some of the top-level categories are mood, plot, genre, time period, praised, based on, etc. Click on any one of these to find sub-categories.

ITVT has an overview article about Jinni. You can access it here.

From the article:

According to the company, the site is designed to help consumers find movies (including movies on DVD and in theaters), TV shows, and Webisodes that match their personal “tastes and moods,” and features a “unique Taste Engine” that combines semantic search, personal recommendations and user profiling.

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Core features of Jinni.com, according to the company, include semantic search (i.e. search that can find results for such semantically/syntactically complex queries as “thought-provoking sci-fi” or “action movie with a surprise twist”), personalized recommendations, and integration with (including the ability to hyperlink to) the catalogs of such content providers as Netflix, Apple iTunes, Amazon, Hulu, and LOVEFiLM, among others. The site also features what Jinni calls the “Movie Personality Sketch,” billed as a visual presentation of the essentials of each user’s unique movie taste; as well as various community features.

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Jinni says that its service is powered by what it calls “the Movie Genome,” which it says contains several thousand “genes” that are assigned to each title to describe mood, style, plot, setting and other features, and that provide “a rich alternative to the usual genre language.” According to the company, new titles are automatically indexed via analysis of user reviews and metadata, using a proprietary “Natural Language Processing” solution. This automated process, the company says, makes for efficiency, consistency and a diversity of viewpoints from analyzing multiple user reviews

Access Jinni

An in-depth look at the The Movie Genome is available. Here’s a small part of the explanation:

Inside, the Genome is broadly 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 and Natural Language Processing, Jinni’s system indexes new titles automatically by analyzing user reviews and metadata. 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 battles to battles as often as possible. It also incorporates multiple perspectives (from reviews) rather than just one person’s opinion. Everyone who votes on genes, as well as the Jinni team, constantly check and improve the machine tagging.