I. Field
Example aspects described herein generally relate to media recommendation and, more particularly, to video recommendation.
II. Related Art
Video suggestions are everywhere on the Internet. They are at the bottom of Netflix pages, Amazon listing, in iTunes, IMDB, YouTube and Google Play. As entertainment consumption moves from physical media rentals and purchases to on-demand or streaming, the importance of contextually-aware recommendation is dramatically increasing as a core driver of user acquisition, engagement and competitive differentiation. Current approaches to video discovery all suffer from the same well-documented problem: contextual blindness.
Collaborative filtering (CF) based recommendation systems look at usage patterns to determine that the same users like two things (“users who did this also did this”). Typical video recommendation systems based on such CF technology, however, have no understanding of the video, music or game they are recommending because they are unable to actually understand the content.
One technical challenge thus involves providing contextually-aware video recommendation. Typical approaches attempt to combat contextual blindness by using a manual, personal editorial curation approach. While this approach provides a level of content-level awareness, it suffers from obvious shortcomings including lack of scalability, cost, and editorial subjectivity.
A technical challenge in providing contextually-aware recommendation to the video domain involves delivering results that actually understand the video(s) being recommended in a way that solves the contextual blindness problem at scale, addressing the shortcomings of manual editorial approaches as well.
Another technical challenge involves providing such contextually aware video recommendations as a complement to existing systems, in order to overlay a contextual understanding of video content atop user-based and personal editorial offerings, thereby eliminating contextual-blindness at scale and offering a much richer, contextually-aware content discovery experience.
USPTO Backgrounds
USPTO Backgrounds