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Concept
Content discovery platform
Summary
A content discovery platform is an implemented software recommendation platform which uses recommender system tools. It utilizes user metadata 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 to television. 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 being established to help academic researchers keep up to date with relevant academic content and serendipitously discover new content.
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 . Bespoke analysis can also be undertaken to understand specific requirements relating to user behavior and activity.
A variety of algorithms can be used:
Collaborative filtering of different users' behavior, preferences, and ratings.
Automatic content analysis and extraction of common patterns.
Social recommendations based on personal choices from other people.
An emerging market for content discovery platforms is academic content. 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. Though traditional tools academic search tools such as Google Scholar or PubMed provide a readily accessible database of journal articles, content recommendation in these cases are performed in a 'linear' fashion, with users setting 'alarms' for new publications based on keywords, journals or particular authors.
Official source
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