A Probabilistic Lifestyle-Based Trajectory Model for Social Strength Inference from Human Trajectory Data

With the pervasiveness of location-based social networks, it becomes increasingly important to consider the social characteristics of locations shared among persons. Several studies have been proposed to infer social strength by using trajectory similarity. However, these studies have two major shortcomings. First, they rely on the explicit co-occurrence of check-in locations. In this situation, a user pair of two friends who seldom share common locations or a user pair of two strangers who heavily share common visited locations will receive an unreliable estimation of the real social strength between them. Second, these studies do not consider how the overall trajectory patterns of users change with the varying of living styles. In this article, we propose a probabilistic generative model to mine latent lifestyle-related patterns from human trajectory data for inferring social strength. It can automatically learn functionality topics consisting of locations with similar service functions and transition probabilities over the set of functionality topics. Furthermore, a lifestyle is modeled as a unique transition probability matrix over the set of functionality topics. A user has a preference distribution over the set of lifestyles, and he or she is able to select over multiple lifestyles to adapt to different living contexts. The learned lifestyle-related patterns are subsequently used as features in a supervised learner for both strength estimation and link prediction. We conduct extensive experiments to evaluate the performance of the proposed method on two real-world datasets. The experimental results demonstrate the effectiveness of our proposed method.