With Adobe Primetime Recommendations, we’ve pioneered an experience where OTT and TV Everywhere viewers can look at the screen, see something they want to watch, and immediately start watching it.
To make this experience a reality, we leveraged the talents of people all throughout our organization, including Gang Wu, a research intern. Wu has worked at Adobe every summer for the past four years while he pursues a Ph.D focused on matrix completion.
This week, StreamingMedia.com published an interview with Wu about his work in enhancing the ability of Adobe Primetime Recommendations to predict which shows a viewer is going to enjoy watching the most. The interview covered how the initial idea came about, the testing of the idea, and what Wu will be working on next.
Here are the key highlights:
- While cleaning and structuring data for Adobe Primetime Recommendations, Wu came up with the idea that more information could be brought to bear in deciding which videos to recommend. Wu modified an algorithm to leverage information like the user’s device, the content, the language of the video, and more. In tests, Wu found that using the context versus not using the context could improve prediction accuracy by up to 20%.
- After positive results with an Adobe Marketing Cloud customer, the Adobe Primetime team implemented Wu’s contextual improvements into the core product.
- Now, Wu is working on a way for Adobe Primetime Recommendations to automatically identify other signals among the detailed session information collected by Adobe Analytics. Wu says, “In the future, we want to make our algorithm capable of automatically picking the information that gets used.”