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

Content Provider	ACM Digital Library
Author	Zhou, Ningnan Zhang, Wenhui Zhao, Wayne Xin Chang, Edward Y. Wen, Ji-Rong Wang, Shan
Copyright Year	2016
Description	Author Affiliation: Renmin University of China, Beijing(Htc research & innovation, Palo Alto, CA (Chang, Edward Y.; Wang, Shan); Wen, Ji-Rong; Zhang, Wenhui; Zhou, Ningnan; Zhao, Wayne Xin)
Abstract	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.
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/2948064
Volume Number	35
Issue Number	1
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Social strength inference Link prediction Probabilistic generative model Trajectory data
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

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