To understand more about how it works, you should check the interview with Digg’s Lead Scientist Anton Kast:
You can also read more about the Recommendation Engine in Anton’s Whitepaper (pdf).
Finding Diggers Like You Explained (excerpt)
The Digg Recommendation Engine uses your Digg history over the last thirty days to
make Recommendations. (You can see the number of items you have Dugg over the
last month on the right-hand side of the Recommended view.) Every time you Digg a
story, the Engine matches you with other Diggers who Dugg the same story, and keeps
track of all your Diggs in common with them.
When it’s time to calculate your Recommendations, the Engine draws from this pool of
matched Diggers. For each matched Digger, it computes a correlation coefficient
between you and them. It then picks a cutoff for this correlation coefficient, and the
Diggers who make the cut are called “Diggers Like You.”
It’s easy to understand how the correlations are calculated. For each user with whom
you Dugg something in common, the Engine determines how many stories the two of
you Dugg in common, and divides that number by the total number of stories you or they
Dugg. The ratio is a correlation coefficient, a number between zero and one (zero if you
and the other user never agreed; one if you always did). Such a ratio is sometimes
called a “Jaccard coefficient.” More here .