To seek suggestions from people we know for deciding the best place to buy a particular good or service. In this work, we put forth a framework of an automated distributed recommendation system on a social network that exploits the widely studied concept of trust, to get personalized responses. The main contribution of our model is to combine two forms in which trust is perceived, the friendship trust and domain-expertise based trail levels, to efficiently propagate a query on a social network. We empirically validate the role of trust in online social networks by crawling the online social networking site Or kut and evaluate our recommendation system against one in which trust has not been used. A novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy.