Recommendations in collaborative tagging sites such as del.icio.us and Yahoo! Movies, are becoming increasingly important, due to the proliferation of general queries on those sites and the ineffectiveness of the traditional search paradigm to address those queries. Regardless of the underlying recommendation strategy, item-based or user-based, one of the key concerns in producing recommendations, is over-specialization, which results in returning items that are too homogeneous. Traditional solutions rely on post-processing returned items to identify those which differ in their attribute values (e.g., genre and actors for movies). Such approaches are not always applicable when intrinsic attributes are not available (e.g., URLs in del.icio.us). In a recent paper [20], we introduced the notion of explanation-based diversity and formalized the diversification problem as a compromise between accuracy and diversity. In this paper, we develop efficient diversification algorithms built upon this notion. The algorithms explore compromises between accuracy and diversity. We demonstrate their efficiency and effectiveness in diversification on two real life data sets: del.icio.us and Yahoo! Movies.