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The prevalence of location-based social networks (LBSNs) has eased theunderstanding of human mobility patterns. Knowledge of human dynamics can aidin various ways like urban planning, managing traffic congestion, personalizedrecommendation etc. These dynamics are influenced by factors like socialimpact, periodicity in mobility, spatial proximity, influence among users andsemantic categories etc., which makes location modelling a critical task.However, categories which act as semantic characterization of the location,might be missing for some check-ins and can adversely affect modelling themobility dynamics of users. At the same time, mobility patterns provide a cueon the missing semantic category. In this paper, we simultaneously address theproblem of semantic annotation of locations and location adoption dynamics ofusers. We propose our model HAP-SAP, a latent spatio-temporal multivariateHawkes process, which considers latent semantic category influences, andtemporal and spatial mobility patterns of users. The model parameters andlatent semantic categories are inferred using expectation-maximizationalgorithm, which uses Gibbs sampling to obtain posterior distribution overlatent semantic categories. The inferred semantic categories can supplement ourmodel on predicting the next check-in events by users. Our experiments on realdatasets demonstrate the effectiveness of the proposed model for the semanticannotation and location adoption modelling tasks.

HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process