Export Compliance Daily is a Warren News publication.

Gracenote Launches Descriptive Metadata Solution to Improve Search, Discovery

Gracenote announced Thursday its next-generation descriptive metadata feature for TV and movie discovery. Gracenote Video Descriptors is the first of the company’s advanced discovery products, designed to improve user engagement for pay-TV providers, over-the-top video services and connected device manufacturers.…

Sign up for a free preview to unlock the rest of this article

Export Compliance Daily combines U.S. export control news, foreign border import regulation and policy developments into a single daily information service that reliably informs its trade professional readers about important current issues affecting their operations.

TV content providers are bolstering catalogs of original and licensed TV shows and movies and developing voice-driven capabilities to meet changing technology, but existing recommendation offerings rely on traditional genre descriptors such as action, comedy and drama that lack personalization, Gracenote said. That puts the onus on viewers to sort through the 40,000 TV episodes and movies to find programing relevant to them, Simon Adams, Gracenote chief product officer, emailed us. The latest Gracenote solution is said to enable clearer understanding of content and more personalized video picks. Genres have been expanded to include mood, theme, scenario and characters, and structured keyword sets for individual shows and movies describe content in progressively more granular terms, Adams said. When the new discovery will appear in consumer products will depend on customer development cycles. TV providers are “fast-tracking” new search and discovery products due to intense competition and pressure to “maximize viewer engagement,” said Adams. The descriptors are available today and can be delivered via IP-delivered software updates. They're powered by machine learning, but Gracenote employs human editors with entertainment knowledge “to train smart algorithms to analyze content at scale,” apply structured descriptors and establish correlations between related TV shows and movies, he said.