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A '''Content Discovery Platform''' is an implemented software recommendation [[Computing platform|platform]] which uses [[recommender system]] tools. It |
A '''Content Discovery Platform''' is an implemented software recommendation [[Computing platform|platform]] which uses [[recommender system]] tools. It utilizes user meta-data in order to discover and recommend appropriate content, whilst reducing ongoing maintenance and development costs. A Content Discovery Platform delivers personalized content to [[website]]s, [[mobile device]]s and [[set-top boxes]]. It consists of key individual modules based on a Relevance Engine [[social search]] that suggests appropriate content according to individual preferences. As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. |
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A content discovery platform utilizes television and [[video on demand]] meta-data in order to discover appropriate content, whilst reducing ongoing maintenance and development costs. A Content Discovery Platform delivers personalized content to [[website]]s, [[mobile device]]s and [[set-top boxes]]. It consists of key individual modules based on a Relevance Engine [[social search]] that suggests appropriate content according to individual preferences. As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. |
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==Methodology== |
==Methodology== |
Revision as of 17:33, 7 December 2014
Recommender systems |
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Concepts |
Methods and challenges |
Implementations |
Research |
A Content Discovery Platform is an implemented software recommendation platform which uses recommender system tools. It utilizes user meta-data in order to discover and recommend appropriate content, whilst reducing ongoing maintenance and development costs. A Content Discovery Platform delivers personalized content to websites, mobile devices and set-top boxes. It consists of key individual modules based on a Relevance Engine social search that suggests appropriate content according to individual preferences. As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator.
Methodology
In order to work with television related content, a search algorithm is used within a Content Discovery Platform to support traditional TV grid views, and also provide TV search results. Personalization and recommendation are tools that promote content effectively within a TV guide; these recommendations are either based on a single show, a set of shows, or a full user profile, and can be used to highlight scheduling, or video On demand, and pay television content within a TV guide. Bespoke analysis can also be undertaken to understand specific requirements relating to user behaviour and activity.
A variety of algorithms can be used:
- Collaborative filtering of different users’ behaviour, preferences, and ratings
- Automatic content analysis and extraction of common patterns
- Social recommendations based on personal choices from other people
On the television set a Content Discovery Platform can be integrated with the network infrastructure and middleware, but does not sit on the Set-top box. By integrating with the network, the platform acts as a cloud computing solution. This means that the solution requires minimal integration, and there are no additional set-top box costs.
Evolving landscape
As the connected television landscape continues to evolve, search & recommendation are seen as having even more pivotal role in the discovery of content.[1] With broadband connected devices, consumers are projected to have access to content from linear broadcast sources as well as internet television. Therefore there is a risk that the market could become fragmented, leaving it to the viewer to visit various locations and find what they want to watch in a way that is time-consuming and complicated for them. By using a search and recommendation engine, viewers are provided with a central ‘portal’ from which to discover content from a number of sources in just one location.