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Tuesday, November 18, 2008

Jinni Helps You Pick The Perfect Flick

by Jason Kincaid

Movies are easier to access than ever, but many of us still struggle with the age old question, “What to watch?” The days of milling around Blockbuster may be gone for many people, but most cable boxes and online movie stores offer little in the way of movie recommendations. Jinni, a new startup launching today in private beta, is looking to help. The site has compiled an index of 10,000 movies and television shows that can be searched using natural language. TechCrunch readers can grab one of 500 exclusive invitations by registering here.

The site has created what it calls “The Movie Genome” - a database of movies tagged by a team of humans aided by a computer algorithm, with attributes spanning fifty categories. The database is reminiscent of Pandora’s Music Genome Project (which is also sorted by human professionals), though it is significantly smaller at this point. Users can either search for movies based on a manual search, browse through movies by their attributes, or can generate recommendations after completing a brief test that determines their movie personality. After finding a movie or show they’re interested in, users can buy or rent them (or in some cases, watch them free) through a number of linked services, including Hulu, Amazon, and Netflix.

In practice the search seems to work well. Each match is visually displayed in a grid as a thumbnail, with the most relevant matches emphasized with larger images. This style makes it easier to quickly identify movies you might be interested in, and also makes false matches less jarring.

Jinni will see heavy competition from existing movie sites like Netflix, which have invested years into developing accurate recommendation algorithms (Netflix even offers a $1 million Prize if anyone can best its algorithm). But Jinni has a fun, intuitive interface and seems to work well, so it may be able to carve out its own slice in the market. Other players in this space include Flixter, which offers social recommendations and TheFilter, which launched movie recommendations earlier this year.