Recommending friends instantly in location-based mobile social networks

Differently from the general online social network (OSN), location-based mobile social network (LMSN), which seamlessly integrates mobile computing and social computing technologies, has unique characteristics of temporal, spatial and social correlation. Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN. However, the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world. In this article, we analyze users´ check-in behavior in a real LMSN site named Gowalla. According to this analysis, we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity, offline behavior similarity and friendship network information in the virtual community simultaneously. This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community. Finally, we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.