Abstract: Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a userís preferences on friend selection in real life. In this paper, we present 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, Friend Recommendation system 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 userís daily life as life documents, from which his/her life styles are extracted by using the Collaborative Filtering with threshold algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate usersí impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friend Recommendation system returns a list of people with highest recommendation scores to the query user. Finally, Friend Recommendation system integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friend Recommendation system on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.

Keywords: Mobile Social Networks, Recommendation friend, Privacy, Collaborative Filtering