Friend recommendation is popular in social network services to help people make new friends and expand their networks. Friend recommendation is either based on topological structures of a social network, or derived from profile information of users. However, dynamically recommending friends by considering both social connections and a context of social connections (e.g., similar interest) in a way of visual exploration is not well supported by existing tools. In this paper, we propose a novel visual system, SFViz (Social Friends Visualization), to support users to explore and find friends interactively under a context of interest. Our approach leverages both semantic structure of activity data and topological structures in social networks. In SFViz, a hierarchical structure of social tags is generated to help users navigate through a network of interest. Multiscale and cross-scale aggregations of similarity among people are presented in the hierarchy to support users to seek potential friends. We report a case study using SFViz to explore the recommended friends based on people's tagging behaviors in a music community, Last.fm. The results indicate that our system can enhance users' awareness of their social networks under different interest contexts, and help users seek potential friends sharing similar interests in an interactive way.