Quantifying the spatial homogeneity of urban road networks via graph neural networks
Quantifying the topological similarities of different parts of urban road networks enables us to
understand urban growth patterns. Although conventional statistics provide useful …
understand urban growth patterns. Although conventional statistics provide useful …
Forecasting traffic flow with spatial–temporal convolutional graph attention networks
X Zhang, Y Xu, Y Shao - Neural Computing and Applications, 2022 - Springer
Traffic flow prediction is crucial for intelligent transportation system, such as traffic
management, congestion alleviation and public risk assessment. Recently, attention …
management, congestion alleviation and public risk assessment. Recently, attention …
POI recommendation with federated learning and privacy preserving in cross domain recommendation
L Wang, Y Wang, Y Bai, P Liu… - IEEE INFOCOM 2021-IEEE …, 2021 - ieeexplore.ieee.org
Point-of-Interest (POI) recommendation is one of the most popular recommendation
methodologies. However, POI data is very sensitive and sparse. Users' reluctance to share …
methodologies. However, POI data is very sensitive and sparse. Users' reluctance to share …
Improving implicit recommender systems with auxiliary data
Most existing recommender systems leverage the primary feedback only, despite the fact
that users also generate a large amount of auxiliary feedback. These feedback usually …
that users also generate a large amount of auxiliary feedback. These feedback usually …
Community preserving social recommendation with Cyclic Transfer Learning
Transfer learning-based recommendation mitigates the sparsity of user-item interactions by
introducing auxiliary domains. Social influence extracted from direct connections between …
introducing auxiliary domains. Social influence extracted from direct connections between …
HOPE: a hybrid deep neural model for out-of-town next POI recommendation
Next Point-of-interest (POI) recommendation has been recognized as an important
technique in location-based services, and existing methods aim to utilize sequential models …
technique in location-based services, and existing methods aim to utilize sequential models …
A deep neural network for crossing-city POI recommendations
D Li, Z Gong - IEEE Transactions on Knowledge and Data …, 2020 - ieeexplore.ieee.org
With the popularity of location-aware devices (eg, smart phones), large amounts of location-
based social media data such as check-ins are generated. This stimulates plenty of studies …
based social media data such as check-ins are generated. This stimulates plenty of studies …
CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature Matching
S Xu, D Guan - Proceedings of the 33rd ACM International Conference …, 2024 - dl.acm.org
Current studies mainly rely on overlapping users (who leave trajectories in both cities) as a
medium to learn travelers' preference in the target city, however it is unrealistic to find …
medium to learn travelers' preference in the target city, however it is unrealistic to find …
TGVx: Dynamic personalized POI deep recommendation model
Personalized points-of-interest (POI) recommendation is very important for improving the
service quality of location-based social network applications. It has become one of the most …
service quality of location-based social network applications. It has become one of the most …
Modeling Long and Short Term User Preferences by Leveraging Multi-Dimensional Auxiliary Information for Next POI Recommendation
Z Li, X Huang, L Gong, K Yuan, C Liu - ISPRS International Journal of Geo …, 2023 - mdpi.com
Next Point-of-Interest (POI) recommendation has shown great value for both users and
providers in location-based services. Existing methods mainly rely on partial information in …
providers in location-based services. Existing methods mainly rely on partial information in …