Video: How to Use Machine Learning to Create Personalized TV Experiences
Learn more about real-world video AI strategies at Streaming Media's next event.
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.
SeaChange International SVP, Strategy Mark Tubinis demonstrates how to use enriched metadata in OTT personalization in this clip from Streaming Media West 2018.
SeaChange International SVP, Strategy Mark Tubinis discusses the difficulty of managing the data necessary to create personalized OTT experiences in this clip from Streaming Media West 2018.
Limelight's Jason Hofmann, Citrix' Josh Gray, and REELY's Cullen Gallagher discuss best practices for training AI systems at Streaming Media East 2018.
Google's Matthieu Lorrain cautions of the risks of doing AI for its own sake in this clip from Streaming Media West 2018.
RealEyes Director of Technology Jun Heider discusses the importance of internal self-assessment and which use-case elements to consider when choosing a platform for video AI in this clip from Streaming Media East 2018.
RealEyes Media Director of Technology Jun Heider identifies the key players in the AI platform space in this clip from Streaming Media East 2018.
RealEyes Director of Technology Jun Heider outlines the first steps in choosing an AI platform in this clip from his presentation at Streaming Media East 2018.
Microsoft Principal Product Manager Rafah Hosn makes the case for reinforcement learning as a machine learning paradigm for content personalization in this clip from Streaming Media East 2018.
Microsoft Principal Product Manager Rafah Hosn discusses the benefits and limitations of a content personalization strategy based on supervised machine learning in this clip from Streaming Media East 2018.
Microsoft Principal Product Manager Rafah Hosn explains how Microsoft's machine learning-driven decision services helps brands target viewers and increase engagement in this clip from Streaming Media East 2018.
Comcast Technical Solutions Architect Ribal Najjar discusses how operationalizing commonalities between QoE and QoS metrics to deliver a "super-powerful" dataset in this clip from Streaming Media East 2018.
Comcast Technical Solutions Architect Ribal Najjar defines video QoE both in terms of subjective experience and qualitative measurement in this clip from Streaming Media East 2018.
IRIS.TV CEO & Co-Founder breaks down discusses IRIS.TV's approach to helping traditional media companies capture and leverage audience data and machine learning in this clip from Streaming Media East 2018.
Gannett Senior Director Kara Chiles discusses how USA Today leveraged IRIS.TV and data to localize and personalize their Winter Olympics 2018 coverage in this clip from Streaming Media East 2018.
REELY CEO Cullen Gallagher makes the business-growth case for content owners developing an AI strategy in this clip from Streaming Media East 2018.
IBM Watson Media's David Clevinger discusses how media entities are currently using video AI in this clip from Streaming Media East 2018.
Citrix Principal Architect Josh Gray explains how video enables higher-acuity metrics analysis in this clip from Streaming Media East 2018.
Limelight VP of Architecture Jason Hofmann discusses how AI impacts content delivery optimization in this clip from Streaming Media East 2018.
Citrix' Josh Gray provides tips on AI model development and Reality Software's Nadine Krefetz and IBM's David Clevinger speculate on the possibilities of metadata-as-a-service in this clip from Streaming Media East 2018.
Google's Leonidas Kantothanassis explores the vast range of applications for machine learning in the media workflow and supply change in this clip from his Content Delivery Summit keynote.
Companies and Suppliers Mentioned