Recommending Nearby Strangers Instantly Based on Similar Check-In Behaviors

Chatting with nearby interested strangers instantly in location-based mobile social network (LMSN) has become increasingly popular. Currently, friend recommendation relies only on the simple and limited user profiles, and is agnostic to users´ offline behaviors in the real world. For the first time, we focus on utilizing the user´s check-in behaviors in the real world, instead of the general acquaintance-based social circles, to instantly recommend nearby strangers to make friends. However, bridging nearby strangers with similar check-in behaviors instantly has some new characteristics, such as lack of common friends and interaction histories, temporal, spatial and user three-dimensional correlation, and sparseness of check-ins. Most existing work about friend recommendations mainly focuses on making friends within the acquaintance-based social circles, and has not fully considered these new characteristics mentioned above. Therefore, how to catch the ephemeral opportunity to recommend nearby interested strangers instantly remains a challenge. In this paper, we present to use “Encounter” probability to measure the behavior similarity of two strangers in the real world based on their check-in histories. To address the sparseness challenge of check-in data, a Kernel Density Estimation (KDE)-based user check-in probability estimation method considering the spatiotemporal dimensions is proposed to estimate each user´s check-in probability distribution with time at each spot. Finally, we use a large-scale user check-in dataset of Gowalla to validate the effectiveness of this approach. The experimental results show that our approach outperforms other commonly used similarity computation methods.