With a mission to personalize every screen, we have released the Ooyala Discovery product. At the core of this release is a set of recommendation algorithms. Recommending video content poses some significant technical challenges: Unlike for text-based content, video keyword generation is manual and therefore quantity and quality can vary widely across providers. Techniques based keywords alone are not sufficient. In the online world, content can quickly become popular, and then just as quickly cool down. The ability to quickly capture user behavior data and apply it timely is crucial. In addition, the engagement data for videos are much more granular than for typical text-based Web pages. Users interact with the videos in different ways: pause, fast forward, replay, and drop off at different time points into a video. Hence, recommendation should be made based on user engagement as opposed to just simple clicks. Further, people watching videos on different devices, and sharing computers and TVs further complicate the problem.
Since we launched Ooyala Discovery, we have significantly improved engagement time for our clients across all forms of content. We offer three types of video recommendation services: trending videos, related videos, and personalized recommendation.
On the technology front, we have made significant advancement to the state-of-the-art, both in terms of data mining algorithms, and system architecture to enable real time feedback. We highlight several of them below:
Since day one, Ooyala has believed that good data is the key to better viewing. We’ve also believed that the future of television isn’t linear broadcasts, but a richer, more engaging, more personalized experience. Perhaps more than any previously released product, Ooyala Discovery represents our dedication to better viewing through data. Stay tuned for more exciting new developments from the Ooyala engineering teams.