Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

Type
Conference Paper

Authors
Alharbi, Basma Mohammed
Zhang, Xiangliang

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program

Online Publication Date
2017-02-07

Print Publication Date
2016-12

Date
2017-02-07

Abstract
Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals's mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users' check-ins and their network of friends, without impairing the model's complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

Citation
Alharbi B, Zhang X (2016) Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties. 2016 IEEE 16th International Conference on Data Mining (ICDM). Available: http://dx.doi.org/10.1109/icdm.2016.0090.

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
2016 IEEE 16th International Conference on Data Mining (ICDM)

DOI
10.1109/icdm.2016.0090
10.1109/ICDM.2016.104

Additional Links
http://ieeexplore.ieee.org/document/7837903/

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