2 research outputs found
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process
The prevalence of location-based social networks (LBSNs) has eased the
understanding of human mobility patterns. Knowledge of human dynamics can aid
in various ways like urban planning, managing traffic congestion, personalized
recommendation etc. These dynamics are influenced by factors like social
impact, periodicity in mobility, spatial proximity, influence among users and
semantic 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 the
mobility dynamics of users. At the same time, mobility patterns provide a cue
on the missing semantic category. In this paper, we simultaneously address the
problem of semantic annotation of locations and location adoption dynamics of
users. We propose our model HAP-SAP, a latent spatio-temporal multivariate
Hawkes process, which considers latent semantic category influences, and
temporal and spatial mobility patterns of users. The model parameters and
latent semantic categories are inferred using expectation-maximization
algorithm, which uses Gibbs sampling to obtain posterior distribution over
latent semantic categories. The inferred semantic categories can supplement our
model on predicting the next check-in events by users. Our experiments on real
datasets demonstrate the effectiveness of the proposed model for the semantic
annotation and location adoption modelling tasks.Comment: 11 page