Ready for your close-up? Jinni goes into public beta
Most of us choose movies based on rather amorphous criteria like mood or an association with another movie we like. Categorisations like genre are too wide; titles are too specific. Jinni approaches discovery in an intuitive way. You can search for movies and TV shows based on mood terms like “witty”, “stylized” or “disturbing” or plot elements like “unlikely couple” or “ambition”. The results are presented visually (we are seaching video after all) with more popular results getting bigger images. You can also tune results to request lesser known titles or faster paced content.
Links are provided to sites like Amazon, Netflix and iTunes (and for European users LOVEFilm) where you can buy or rent the content. The recommendation system learns rapidly based on ratings you give for the movies you find in the first searches. Within a few minutes it was coming up with recommendations for unfamiliar content as well as movies I already knew and liked. Jinni expects to offer high quality recommendations after 10-20 ratings. The database currently contains 25,000 titles, 20% of which are non-English language.
What’s New Pussycat?
If you click on another user, you will see your shared tastes like “You both especially like clever stories about betrayal and gangsters” as well as movies you both like. Other community features like social recommendations and the pulse (what’s happening on Jinni right now) have also been introduced.
The Matrix (aka the Science Bit)
The core of Jinni is a “movie genome” similar to Pandora’s music genome, which categorises video content using 2200 different parameters or “genes” covering mood, style, setting, atmosphere, etc. Natural language processing and sentiment analysis to used to derive the genes from reviews and other information available online about the movie. The combination of algorithms used to extract the genes, determine their relevance and use them in recommendations is Jinni’s golden egg and a closely guarded secret. This is not an easy problem given the subtleties of language and meaning (“Is dark humour the same as irreverent humour?” “What is the distinction between assassin, serial killer and slasher?”) not to mention the unorganised nature of the web data which is used. It’s refreshing to see a startup which is actually tackling a difficult technical problem. Crowdsourcing is not the answer to everything.
Jinni has several advantages over other recommendation engines. As well as offering good recommendations quickly, it can explain why a particular movie was recommended based on the most relevant genes and even point out elements of your movie taste of which you may not have been aware. In contrast, collaborative filtering can only tell you that someone who has similar taste to you liked the movie.
A minimum of 4 reviews is required to analyze a title and even long-tail titles have typically been reviewed many more times. This method means that a title can be indexed even if it has never been watched by a Jinni user.
Top Gun
There are many other recommendation engines (e.g. Tastekid, Clerkdogs, Criticker) but none of them seems to cover all the functionality available in Jinni or match its pleasing design. The closest is spirit to Jinni is probably Nanocrowd which uses its own 3 word genome called the nanogenre. An example is happiness-struggles-pretentious which apparently applies to “The Hours”.
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