Microsoft Announces Jinni as Xbox Video Discovery Engine

Video content discovery solutions provider Jinni, announced today a multi-year agreement with Microsoft to enhance entertainment discovery on Xbox video game and entertainment systems. Jinni’s proprietary Entertainment Genome™ uses deep knowledge of every show and movie in the Xbox Video catalog to help the Xbox recommendations system intuitively and accurately connect Xbox users to content they’ll enjoy.  By using the rich set of attributes in the Jinni Entertainment Genome, Xbox recommendations go beyond the standard genre similarity. When paired with other Xbox signals, such as a user’s viewing history, this sets a new bar in content discovery.
 
“Creating the most amazing entertainment experience means always putting the customer experience first,” said Dave Alles, Xbox General Manager. “Our goal is to make it effortless to get you to entertainment you’ll truly love.  Pairing Jinni’s Entertainment Genome™ with other key advances such as Conversational Understanding, makes finding something to watch on Xbox as fun as watching it.”
 
“The inspiration for Jinni has always been putting the user at the center of the entertainment experience, by making finding great content intuitive and fun,” said Jinni Co-founder and CEO, Yosi Glick. “We are happy to be a part of the legendary Xbox entertainment experience.”
 
About Jinni
Jinni is the first and only taste-and-mood based engine powering entertainment discovery. Using content genetics and nuanced understanding of user tastes, the Jinni engine powers a uniquely intuitive and personalized experience that increases content consumption and consumer satisfaction.
 
The Jinni service is powered by the Entertainment Genome™, containing thousands of genes that are assigned to each title to describe mood, style, plot, setting and more; this is a rich alternative to the
usual genre language, which benefits both the quality of the content delivered as well as the intuitive semantic-based user experience. New titles are automatically indexed via analysis of user reviews and synopses, using a proprietary Natural Language Processing solution.