Video: How to Use Machine Learning to Create Personalized TV Experiences
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Watch the complete video of this panel, AI102; Case Studies: Breaking New Ground in AI, in the Streaming Media Conference Video Portal.
Read the complete transcript of this clip:
Tom Sauer: The TV world today, as we think about it, we believe needs a real overhaul. Think about what the current MSO satellite world out: there it's been unchanged for many, many years. The networks and the operators really control the experience. There's not a lot of personalization. Maybe some personalization in the broad catalog, but for the most part, it truly is a lean-back-and-hope-to-DVR-the-right-show-at-the-right-time, so that you can go back and watch it. Of course, TV everywhere solves some of that. Then, we enter this, the new TV world space, and these are really the OVP's and the virtual MVPDs out there. So we've got a lot of capabilities around recommendations, collaborative recommendations, better access to finding content, but we're still out there searching for content.
There's limited personalization. It's still either that somewhat-lean-back experience, or it's paging through screens and screens trying to find exactly what you want to watch, if it's not something that you DVR'd or captured. So we believe that there's a huge need out there today to move to what we call “NextGen TV.” And so, NextGen TV isn't a new platform, it's really about a new experience. It's about an experience that can transcend across these current platforms out there, and most importantly we believe will revolutionize the old TV space.
We'll talk about what we're doing with Ooyala-Microsoft in the AI space to create an effortless experience. We're using AI to curate our channels, as I mentioned earlier. We will create a uniquely personalized experience.
If we think about the new TV world and this NextGen TV world. The new TV world is really still at a recommendation based system for discovering content and presenting what we call today, personalized experiences. So, the recommendations--it's the act of saying that something is good and deserves to be chosen. There are a lot of different algorithms out there today that are used to achieve this. Popularity-type algorithms, recommending the most popular thing. Identifying similar items, item-based type, so for a collaborative filtering predicting what a user might want based on a collection of preferences or tastes from a broader of set of users. But now, that all worked pretty well, and we all get reasonably good recommendations from the various sites that we use. But we need to move to the next level: a truly personalized experience.
We believe that in order to do that, we can take machine learning, and we can couple that with recommendations, and we get what will ultimately be a uniquely personalized experience.
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