Self-supervised contrastive representation learning for large-scale trajectories
Trajectory representation learning aims to embed trajectory sequences into fixed-length
vector representations while preserving their original spatio-temporal feature proximity …
vector representations while preserving their original spatio-temporal feature proximity …
Pre-Training General Trajectory Embeddings With Maximum Multi-View Entropy Coding
Spatio-temporal trajectories provide valuable information about movement and travel
behavior, enabling various downstream tasks that in turn power real-world applications …
behavior, enabling various downstream tasks that in turn power real-world applications …
Trajectory distribution aware graph convolutional network for trajectory prediction considering spatio-temporal interactions and scene information
Pedestrian trajectory prediction has been broadly applied in video surveillance and
autonomous driving. Most of the current trajectory prediction approaches are committed to …
autonomous driving. Most of the current trajectory prediction approaches are committed to …
Contrastive pre-training with adversarial perturbations for check-in sequence representation learning
A core step of mining human mobility data is to learn accurate representations for user-
generated check-in sequences. The learned representations should be able to fully describe …
generated check-in sequences. The learned representations should be able to fully describe …
MCN4Rec: Multi-level Collaborative Neural Network for Next Location Recommendation
Next location recommendation plays an important role in various location-based services,
yielding great value for both users and service providers. Existing methods usually model …
yielding great value for both users and service providers. Existing methods usually model …
Deeproute+: Modeling couriers' spatial-temporal behaviors and decision preferences for package pick-up route prediction
Over 10 billion packages are picked up every day in China. A fundamental task raised in the
emerging intelligent logistics systems is the couriers' package pick-up route prediction …
emerging intelligent logistics systems is the couriers' package pick-up route prediction …
PreCLN: Pretrained-based contrastive learning network for vehicle trajectory prediction
Trajectory prediction of vehicles is of great importance to various smart city applications
ranging from transportation scheduling, vehicle navigation, to location-based …
ranging from transportation scheduling, vehicle navigation, to location-based …
Contrastive pre-training of spatial-temporal trajectory embeddings
Pre-training trajectory embeddings is a fundamental and critical procedure in spatial-
temporal trajectory mining, and is beneficial for a wide range of downstream tasks. The key …
temporal trajectory mining, and is beneficial for a wide range of downstream tasks. The key …
Pre-training contextual location embeddings in personal trajectories via efficient hierarchical location representations
Pre-training the embedding of a location generated from human mobility data has become a
popular method for location based services. In practice, modeling the location embedding is …
popular method for location based services. In practice, modeling the location embedding is …
Pre-Training Across Different Cities for Next POI Recommendation
The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative
sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across …
sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across …