Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2017-02-07Online Publication Date
2017-02-07Print Publication Date
2016-12Permanent link to this record
http://hdl.handle.net/10754/623861
Metadata
Show full item recordAbstract
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.Additional Links
http://ieeexplore.ieee.org/document/7837903/ae974a485f413a2113503eed53cd6c53
10.1109/icdm.2016.0090