Predicting human mobility via self-supervised disentanglement learning
Deep neural networks have recently achieved considerable improvements in learning
human behavioral patterns and individual preferences from massive spatial-temporal …
human behavioral patterns and individual preferences from massive spatial-temporal …
Mobility prediction via sequential trajectory disentanglement (student abstract)
Accurately predicting human mobility is a critical task in location-based recommendation.
Most prior approaches focus on fusing multiple semantics trajectories to forecast the future …
Most prior approaches focus on fusing multiple semantics trajectories to forecast the future …
Predicting collective human mobility via countering spatiotemporal heterogeneity
Human mobility forecasting is the key to energizing considerable mobile computing
services. However, we find that the collective mobility suffers the spatiotemporal …
services. However, we find that the collective mobility suffers the spatiotemporal …
Deepmove: Predicting human mobility with attentional recurrent networks
Human mobility prediction is of great importance for a wide spectrum of location-based
applications. However, predicting mobility is not trivial because of three challenges: 1) the …
applications. However, predicting mobility is not trivial because of three challenges: 1) the …
Predicting human mobility via long short-term patterns
J Chen, J Li, Y Li - Computer Modeling in Engineering & …, 2020 - ingentaconnect.com
Predicting human mobility has great significance in Location based Social Network
applications, while it is challenging due to the impact of historical mobility patterns and …
applications, while it is challenging due to the impact of historical mobility patterns and …
Inferring individual human mobility from sparse check-in data: a temporal-context-aware approach
Inferring individual human mobility at a given time is not only beneficial for personalized
location-based services but also crucial for tracking trajectory of the confirmed cases in the …
location-based services but also crucial for tracking trajectory of the confirmed cases in the …
MobTCast: Leveraging auxiliary trajectory forecasting for human mobility prediction
Human mobility prediction is a core functionality in many location-based services and
applications. However, due to the sparsity of mobility data, it is not an easy task to predict …
applications. However, due to the sparsity of mobility data, it is not an easy task to predict …
Learning to Generate Pseudo Personal Mobility
The importance of personal mobility data is widely recognized in various fields. However,
the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to …
the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to …
Self-supervised human mobility learning for next location prediction and trajectory classification
Massive digital mobility data are accumulated nowadays due to the proliferation of location-
based service (LBS), which provides the opportunity of learning knowledge from human …
based service (LBS), which provides the opportunity of learning knowledge from human …
Adversarial mobility learning for human trajectory classification
Understanding human mobility is one of the important but challenging tasks in Location-
based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User …
based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User …