This week social-news site Digg is launching a new way for users to find new stories by adding a recommendation engine that will suggest things for users to read in the upcoming story section based on past site usage. It will take into account what other people similar to yourself are digging on the site and add a special note that shows how many stories you and that person share in common.
The move is two-fold: One part is to expand the social network that has been Digg since the launch of its expanded profile system back in September. The other is to give upcoming stories a little more attention. Stories submitted to Digg can reach the site's front page a number of ways, either by being linked up to highly trafficked external sites, or simply by natural selection in Digg's upcoming queue. The updated recommendation engine will simply be a new way for those stories to get more eyeballs on them given that the number of submitted stories averages around 16,000 a day, according to founder Kevin Rose.
The new feature is only being rolled out to a random number of registered Digg users and is expected to make its way to everyone else in the months to come. In the meantime, you can get some suggestions for stories you might like based on your previous "digging" habits using a third-party service called DiggSuggest. It doesn't use the same algorithm, or do it passively and onsite like Digg's does, but it comes with some pretty interesting results.
You can see the new feature in action in the explanatory video below. After the break, there's also a video with Digg's chief scientist Anton Kast explaining in detail how the new system works.
Digg Recommendation Engine from Kevin Rose on Vimeo.
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