With the popularity of social network and the increasing number of Web Services, making individual service recommendation has been a hot research spot nowadays. In this paper, we present a service recommendation algorithm named as URPC-Rec (User Relationships & Preferences Clustering and Recommendation), which first clusters users based on their history behaviors such as the services they ever invoked, and then makes personalized recommendations for users considering both the clustering results and user basic information and relationships, such as gender, age, occupation, preference tags, etc. The case study indicates that URPC-Rec can effectively reduce the dimensionality of sparse matrix, and partially solve the cold-start problem of recommendation systems. The comprehensive experiment shows that URPC-Rec algorithm with user relationships and references has better recommending result than the one without user information and the collaborative filtering approach.