Collaborative filtering attempts to find items of interest for a user by utilizing the preferences of other users. In this paper we describe an approach to filtering that explicitly uses social relationships, such as friendship, to find items of interest to a user. Modeling user-item relations as a bipartite graph we augment it with user-user (social) links and propose an absorbing random walk that induces a set of stationary distributions, one per user, over all items. These distributions can be interpreted as personalized rankings for each user. We exploit sparsity of both user-item and user-user relationships to improve the efficiency of our algorithm.