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Video Breakthroughs
Monitoring innovations in post-production, head-end, streaming, OTT, second-screen, UHDTV, multiscreen strategies & tools
Curated by Nicolas Weil
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Netflix: System Architectures for Personalization and Recommendation

Netflix: System Architectures for Personalization and Recommendation | Video Breakthroughs | Scoop.it

In our previous posts about Netflix personalization, we highlighted the importance of using both data and algorithms to create the best possible experience for Netflix members. We also talked about the importance of enriching the interaction and engaging the user with the recommendation system. Today we're exploring another important piece of the puzzle: how to create a software architecture that can deliver this experience and support rapid innovation. Coming up with a software architecture that handles large volumes of existing data, is responsive to user interactions, and makes it easy to experiment with new recommendation approaches is not a trivial task. In this post we will describe how we address some of these challenges at Netflix.

Nicolas Weil's insight:

A major contribution in the OTT architecture information pool.

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TDF launches first end-to-end HbbTV platform

TDF launches first end-to-end HbbTV platform | Video Breakthroughs | Scoop.it

TDF Media Services has announced the launch of the first end-to-end HbbTV platform. This new platform for broadcasters and media companies is able to support any kind of video-based hybrid TV services. Thanks to the HbbTV technologies, it is now possible to enrich a live broadcast stream with additional internet content delivered directly through the TV set.

 

The platform is compatible with HbbTV1.1 and HbbTV1.5 and supports both Marlin and Microsoft Playready DRMs. It also provides a full end-to-end solution, including metadata management, video processing, content monetisation. Content Delivery Network provided by SmartJog enables a smooth and very high quality video streaming to all connected devices including HbbTV connected TV sets.

 

Other advanced features have been integrated to enrich the end-user experience, such as a recommendation engine provided by French technology company Cognik, an artificial intelligence engine which selects and proposes relevant content to a specific user.

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Netflix Recommendations: Beyond the 5 stars (Part 2)

Netflix Recommendations: Beyond the 5 stars (Part 2) | Video Breakthroughs | Scoop.it

In part one of this blog post, we detailed the different components of Netflix personalization. We also explained how Netflix personalization, and the service as a whole, have changed from the time we announced the Netflix Prize.The $1M Prize delivered a great return on investment for us, not only in algorithmic innovation, but also in brand awareness and attracting stars (no pun intended) to join our team. Predicting movie ratings accurately is just one aspect of our world-class recommender system. In this second part of the blog post, we will give more insight into our broader personalization technology. We will discuss some of our current models, data, and the approaches we follow to lead innovation and research in this space.

 

The goal of recommender systems is to present a number of attractive items for a person to choose from. This is usually accomplished by selecting some items and sorting them in the order of expected enjoyment (or utility). Since the most common way of presenting recommended items is in some form of list, such as the various rows on Netflix, we need an appropriate ranking model that can use a wide variety of information to come up with an optimal ranking of the items for each of our members.

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Netflix Recommendations: Beyond the 5 stars (Part 1)

Netflix Recommendations: Beyond the 5 stars (Part 1) | Video Breakthroughs | Scoop.it

In this two-part blog post, we will open the doors of one of the most valued Netflix assets: our recommendation system. In Part 1, we will relate the Netflix Prize to the broader recommendation challenge, outline the external components of our personalized service, and highlight how our task has evolved with the business. In Part 2, we will describe some of the data and models that we use and discuss our approach to algorithmic innovation that combines offline machine learning experimentation with online AB testing.

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Digitalsmiths Rolls Out "Seamless Discovery" Platform for Video Recommendations

Digitalsmiths Rolls Out "Seamless Discovery" Platform for Video Recommendations | Video Breakthroughs | Scoop.it

Late last week, video search and recommendation technology provider Digitalsmiths quietly introduced its "Seamless Discovery" platform, targeted to pay-TV operators, consumer electronics companies and content producers who want to deliver highly relevant recommendations to their users. The platform addresses the urgent problem that users are fragmenting their viewing over multiple devices where the discovery experience is inconsistent and lacking.

 

In a phone briefing, Digitalsmiths' CEO Ben Weinberger explained that a key differentiator for Seamless Discovery is that it draws from multiple data sets in order to provide recommendations, resulting in improved relevance. At the core is metadata Digitalsmiths creates on the programming available from the pay-TV operator, CE device or content owner. For pay-TV operators specifically, this involves ingesting full schedule information from sources like Tribune Media Services. This metadata is mapped with contextual and behavioral data and "social graph" information from Facebook along with other inputs. The system learns over time from the choices the user makes which of these factors is most relevant, tweaking future recommendations accordingly.

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How to Guide: Content Recommendation & Discovery

How to Guide: Content Recommendation & Discovery | Video Breakthroughs | Scoop.it
Through interviews with the BBC, Blip TV and ThinkAnalytics we look at four approaches to providing great content discovery and recommendations tools to your users.
Nicolas Weil's insight:

See also a short list of recommendation engines on my blog here : http://goo.gl/C6XQe

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Digitalsmiths Announces New Mood-Based Video Content Discovery Feature [PR]

Digitalsmiths Announces New Mood-Based Video Content Discovery Feature [PR] | Video Breakthroughs | Scoop.it

Seamless Discovery mood-­based recommendations are powered by a deeper level of intelligence around video content. By leveraging the world’s largest collection of scene-­level, time-­based video data from Digitalsmiths and industry-­standard data sets from Tribune Media Service (TMS) and other 3rd parties, Seamless Discovery delivers the industry’s most accurate mood-­based recommendations.

 

SEE ALSO SPIDEO'S SOLUTIONS : http://www.spideo.tv/

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Why is Discovery so hard to implement for video services?

Why is Discovery so hard to implement for video services? | Video Breakthroughs | Scoop.it

Last week, Google said it was trying to tackle one of the hardest problems on the internet -- video Discovery.

 

Looking at consumer video services (Netflix, Hulu, Amazon and even GoogleTV) and their second screen counterparts (Matcha, Fanhattan, BuddyTV, etc), the admission of the challenge is painfully evident in the user interface the consumer faces and the result of the Discovery process.

 

But let's back up a bit first. What is Discovery? How does it relate to Search and Recommendation? I think we will find wide agreement that the concept of Search is one where you know what you are looking for and are trying to find it. Now this can be more complex than "Where can I find a legal version of Mission Impossible: Ghost Protocol that I can watch in my living room right now?" (which itself can be challenging in today's service offerings). It is not usually as complex as the problem Shazam solves in the music industry ("what is the name of that song that sounds like..."), but can be difficult (I know the actor who was in the movie or what it was about). Search is decidedly a "lean forward" experience, and as most of us have found out over the last 5 years, it incredibly difficult to implement on a 10-foot remote experience, with various virtual keyboards or fancy remotes trying to help us solve this problem.

 

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Jinni Offers Enhanced API, Builds in Social TV Recommendations

Jinni Offers Enhanced API, Builds in Social TV Recommendations | Video Breakthroughs | Scoop.it

Jinni has announced an enhanced version of its API, letting TV and device makers add more personalization and social recommendation features to their movie and TV show guides. Jinni, a Tel Aviv, Israel-based company, creates search and discovery recommendations based on the viewer's mood and taste.

 

Capitalizing on one of the biggest trends in online video, the API now offers social recommendations from friends that also match the viewer's profile. Jinni doesn't rate all friend recommendations equally, but looks for taste matches. The company previewed this feature at CES, and is now releasing it.

 

Jinni can now recommend programming from live TV, a big step for a product that previously only offered video-on-demand and over-the-top content recommendations.

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