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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]]. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles <ref> |
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]]. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles <ref>http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806</ref> to television.<ref>http://www.wired.com/2011/12/netflix-revamps-ipad-app-to-improve-content-discovery/</ref> As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. Academic content discovery has recently become another area of interest, with several companies, such as [[Sparrho]],<ref>http://www.sparrho.com/about/</ref> being established to help academic researchers keep up to date with relevant academic content and serendipitously discover new content.<ref>http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806</ref> |
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==Methodology== |
==Methodology== |
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In to provide and recommend content, a [[search algorithm]] is used within a Content Discovery Platform to provide keyword related search results. User [[personalization]] and recommendation are tools that are used in the determination of appropriate content; these recommendations are either based on a single article or show, a particular academic field or genre of TV, or a full [[user profile]]. Bespoke analysis can also be undertaken to understand specific requirements relating to user behaviour and activity. |
In to provide and recommend content, a [[search algorithm]] is used within a Content Discovery Platform to provide keyword related search results. User [[personalization]] and recommendation are tools that are used in the determination of appropriate content; these recommendations are either based on a single article or show, a particular academic field or genre of TV, or a full [[user profile]]. Bespoke analysis can also be undertaken to understand specific requirements relating to user behaviour and activity. |
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A variety of algorithms can be used: |
A variety of algorithms can be used: |
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* [[Collaborative filtering]] of different users’ behaviour, preferences, and ratings |
* [[Collaborative filtering]] of different users’ behaviour, preferences, and ratings |
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* Automatic content analysis and extraction of common patterns |
* Automatic content analysis and extraction of common patterns |
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* Social recommendations based on personal choices from other people |
* Social recommendations based on personal choices from other people |
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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. |
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. |
Revision as of 08:24, 8 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. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles [1] to television.[2] As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. Academic content discovery has recently become another area of interest, with several companies, such as Sparrho,[3] being established to help academic researchers keep up to date with relevant academic content and serendipitously discover new content.[4]
Methodology
In to provide and recommend content, a search algorithm is used within a Content Discovery Platform to provide keyword related search results. User personalization and recommendation are tools that are used in the determination of appropriate content; these recommendations are either based on a single article or show, a particular academic field or genre of TV, or a full user profile. 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.[5] 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.