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Digg announces new mobile networking platform and increased traffic

Staff (Social Media Portal) - 01 August 2008

In an entry posted yesterday on the company’s official blog, social recommendation platform Digg announced the launching of a new, enhanced version of its mobile networking platform.
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Redesigned to better exploit the more robust browsing capabilities of the most recent web-enabled hand-held devices such as the iPhone 3G or those produced by manufacturers such as Treo, Blackberry, m.digg.com offers a number of new features.  These include a filter system that enables users to access multiple views of the most popular stories on Digg, better referencing of media categories, the ability to mark them as ‘favourites” and an increase in the number of comments that can be posted on each.  Lesser recourse to javascript is alleged to improve page-loading times.  For phones that cannot handle the higher-speed connection required to access the new site, Digg announces that it will continue to run its more basic mobile networking platform, diggriver.com.

The new offering comes at a time when Digg claims that the Recommendation Engine the site rolled out a month ago is showing signs of early success, quoting a 40% increase in the total number of ‘Diggs’ and friend activity up by 24% with each site user (or ‘Digger’) receiving nearly 200 Recommendations from an average of 34 “Diggers like you”.

Writing on the company’s official blog, Anton from Digg concludes this list of figures by inviting users to give their own opinion on how the site is evolving, commenting that, “If you have feedback on the Recommendation Engine, we’d like to hear from you by filling out this quick survey or by adding your comments below. If you have yet to test drive the Recommendation Engine we encourage you to check it out. It’s a great way to discover relevant upcoming content on Digg.”

Further changes are announced for the near future, such as extending the time-frame during which a Digging window can gather recommendations, and perfecting the algorithm that drives recommendations for improved targeting.


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