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

Basma Mohammed Alharbi, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.
Original languageEnglish (US)
Title of host publication2016 IEEE 16th International Conference on Data Mining (ICDM)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781509054732
DOIs
StatePublished - Feb 7 2017

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