Editing Recommender system (section)

===Academic content discovery===
An emerging market for content discovery platforms is academic content.<ref>{{cite journal |last1=Mirkin |first1=Sima |date=2014-06-04 |title="Extending and Customizing Content Discovery for the Legal Academic Com" by Sima Mirkin |url=http://digitalcommons.wcl.american.edu/facsch_lawrev/253/ |journal=Articles in Law Reviews & Other Academic Journals |publisher=Digital Commons @ American University Washington College of Law |accessdate=2015-12-31}}</ref><ref>{{cite web|url=http://www.publishingtechnology.com/2013/04/mendeley-elsevier-and-the-importance-of-content-discovery-to-academic-publishers/ |accessdate=December 8, 2014 |url-status=dead |archiveurl=https://web.archive.org/web/20141117210156/http://www.publishingtechnology.com/2013/04/mendeley-elsevier-and-the-importance-of-content-discovery-to-academic-publishers/ |archivedate=November 17, 2014 |title=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 name="nature1"/> 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.

Google Scholar provides an 'Updates' tool that suggests articles by using a [[statistical model]] that takes a researchers' authorized paper and citations as input.<ref name="nature1"/> 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 name="nature1"/>