Social bookmarking and news aggregator Digg has announced that it will integrate a new recommendations engine. This will track users’ activity and recommend stories according to the behavior of other presumably like-minded users.

This is mostly a filter to increase relevance — a move toward letting users cut through the clutter, now that “Dug” stories are reaching 12,000 to 14,000 per day.

This will be a passive recommendations engine by automatically formulating the stories you might like, and the list of “Diggers Like You” (ranked by compatibility percentage).

But it will also allow for a certain amount of manual override by letting users delete those whose diggs they might not like. And they can also choose to forgo the feature altogether and go back to the traditional list of top diggs from the overall community.

The feature not only brings an additional layer of social relevance to the service, but also seems to bring it a step closer to new forms of monetization. These could include ad placements based on behavioral targeting from users’ level of interaction with news stories and active diggs.

It will also generate a social graph, which has implications for ad targeting, as shown by early monetization efforts by and within other social networks like Facebook. Check out a video demo of the new feature and an explanation from founder Kevin Rose on the Digg blog.