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A Probabilistic Lifestyle-Based Trajectory Model for Social Strength Inference from Human Trajectory Data by Wayne Xin Zhao; Ningnan Zhou; Wenhui Zhang; Ji-Rong Wen; Shan Wang; Edward Y. Chang is a Computer Science article available to read on EtoBox.

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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

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Author

Wayne Xin Zhao; Ningnan Zhou; Wenhui Zhang; Ji-Rong Wen; Shan Wang; Edward Y. Chang

Publisher

ACM

Published

2016

Language

EN

Field

Computer Science (Physical Sciences)