The rapidly increasing popularity of Web 2.0 knowledge and content
sharing systems and growing amount of shared data make discovering
relevant content and finding contacts a difficult enterprize.
Typically, folksonomies provide a rich set of structures and social
relationships that can be mined for a variety of recommendation
purposes. We propose a novel formal model for characterizing
users, items, and annotations in Web 2.0 environments. Our objective
is to construct social recommender systems that predict the utility
of items, users, or groups based on the multi-dimensional social
environment of a given user.