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==Academic Content Discovery== |
==Academic Content Discovery== |
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An emerging market for content discovery platforms is academic content <ref>http://digitalcommons.wcl.american.edu/facsch_lawrev/253/</ref><ref>http://www.publishingtechnology.com/2013/04/mendeley-elsevier-and-the-importance-of-content-discovery-to-academic-publishers/</ref> Approximately 6000 academic journal articles are published daily, making it increasingly difficult for researchers to balance time management with staying up to date with relevant research <ref>http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806</ref>. Though traditional tools academic search tools such as [[Google Scholar]] or [[PubMed]] provide a readily accessible database of journal articles, content recommendation is performed in a 'linear' fashion, with users setting 'alarms' for new publications based on keywords, journals or particular authors. Google Scholar provides an 'Updates' tool which |
An emerging market for content discovery platforms is academic content <ref>http://digitalcommons.wcl.american.edu/facsch_lawrev/253/</ref><ref>http://www.publishingtechnology.com/2013/04/mendeley-elsevier-and-the-importance-of-content-discovery-to-academic-publishers/</ref> Approximately 6000 academic journal articles are published daily, making it increasingly difficult for researchers to balance time management with staying up to date with relevant research <ref>http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806</ref>. Though traditional tools academic search tools such as [[Google Scholar]] or [[PubMed]] provide a readily accessible database of journal articles, content recommendation is these cases performed in a 'linear' fashion, with users setting 'alarms' for new publications based on keywords, journals or particular authors. Google Scholar provides an 'Updates' tool which can suggest articles by using a statistical model which takes a researcher's authored papers and citations as input <ref>http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806</ref>. Whilst these recommendations have been noted to be extremely good, this poses a problem with early career researchers which may be lacking a sufficient body of work to produce accurate recommendations <ref>http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806</ref>. |
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==Evolving landscape== |
==Evolving landscape== |
Revision as of 23:01, 9 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. 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
Academic Content Discovery
An emerging market for content discovery platforms is academic content [5][6] Approximately 6000 academic journal articles are published daily, making it increasingly difficult for researchers to balance time management with staying up to date with relevant research [7]. Though traditional tools academic search tools such as Google Scholar or PubMed provide a readily accessible database of journal articles, content recommendation is these cases performed in a 'linear' fashion, with users setting 'alarms' for new publications based on keywords, journals or particular authors. Google Scholar provides an 'Updates' tool which can suggest articles by using a statistical model which takes a researcher's authored papers and citations as input [8]. Whilst these recommendations have been noted to be extremely good, this poses a problem with early career researchers which may be lacking a sufficient body of work to produce accurate recommendations [9].
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.[10] 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.
References
- ^ http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806
- ^ http://www.wired.com/2011/12/netflix-revamps-ipad-app-to-improve-content-discovery/
- ^ http://www.sparrho.com/about/
- ^ http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806
- ^ http://digitalcommons.wcl.american.edu/facsch_lawrev/253/
- ^ http://www.publishingtechnology.com/2013/04/mendeley-elsevier-and-the-importance-of-content-discovery-to-academic-publishers/
- ^ http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806
- ^ http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806
- ^ http://www.nature.com/news/how-to-tame-the-flood-of-literature-1.15806
- ^ The New Face of TV