Jinni Mood-Based Discovery Engine for Movies and TV shows Does Deal with Microsoft

Posted by Richard Kastelein in Breaking News on September 10, 2011  |  0 Comments More from this author  |    |  Follow Richard Kastelein on: Facebook | LinkedIn | Twitter | YouTube

Jinni, the innovators of the only taste-and-mood based discovery engine for movies and TV shows, today announced an agreement with Microsoft Corp. to license semantic discovery technology for personalized, holistic discovery of video entertainment.

"Microsoft's choice to license the Jinni video discovery solution further validates our unique semantic technology and holistic approach to entertainment discovery" said Yosi Glick, CEO and Co-founder of Jinni. "At Jinni we believe finding digital entertainment should be as natural and enjoyable as consuming it. Our technology can power a wide variety of intuitive discovery tools, including; semantic search, personalized recommendations, and mood-based discovery features for a fun way to find movies and TV."

Jinni Semantic Taste Discovery

The Semantic Discovery Engine and Entertainment Genome, a rich set of more than 2,000 tags to describe the mood, plot and style of any title, was developed specifically for discovery of video content. The Jinni engine enables a variety of meaning-based discovery features to help users find what to watch on any screen, any time. Jinni's Entertainment Genome enables exceptionally intuitive, explainable and holistic video discovery to power any screen with a suite of features for the next generation of digital home entertainment guides.

Jinni offers unique features, such as:

·         Mood-based Discovery – Search or browse within intuitive categories like mood, plot and style to choose content in a large catalog with fewer clicks. For example, just two clicks on 'witty', then 'sibling relationships' gets results http://www.jinni.com/s/Lla8

·         Taste-based Recommendations – Highly personalized recommendations extracted from distinct groups of semantic genes that characterize a user's various tastes. For example, a user may enjoy 'Suspenseful, stylized, rough stories about gangsters, cons and scams and heists'

·         Explainability – Complete transparency builds user trust, so natural language explanations of why each title is recommended are provided

·         Similar Titles/Content Recommendations – Easy, intuitive way to find more shows or movies that are truly similar based on comparison of semantic attributes

·         Effective Social Discovery – Cuts through the buzz to find friends as well as other users with similar taste that can be trusted sources of social recommendations

Learn more about the Jinni Semantic Discovery Engine for Digital Entertainment below.

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