Deep learning for spatio-temporal data mining: A survey

S Wang, J Cao, SY Philip - IEEE transactions on knowledge …, 2020 - ieeexplore.ieee.org
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

A survey on deep learning for human mobility

M Luca, G Barlacchi, B Lepri… - ACM Computing Surveys …, 2021 - dl.acm.org
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …

Stan: Spatio-temporal attention network for next location recommendation

Y Luo, Q Liu, Z Liu - Proceedings of the web conference 2021, 2021 - dl.acm.org
The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

Predicting urban region heat via learning arrive-stay-leave behaviors of private cars

Z Xiao, H Li, H Jiang, Y Li, M Alazab… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Urban region heat refers to the extent of which people congregate in various regions when
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …

Deepmove: Predicting human mobility with attentional recurrent networks

J Feng, Y Li, C Zhang, F Sun, F Meng, A Guo… - Proceedings of the 2018 …, 2018 - dl.acm.org
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 …

Graph-flashback network for next location recommendation

X Rao, L Chen, Y Liu, S Shang, B Yao… - Proceedings of the 28th …, 2022 - dl.acm.org
Next Point-of Interest (POI) recommendation plays an important role in location-based
applications, which aims to recommend the next POIs to users that they are most likely to …

Research commentary on recommendations with side information: A survey and research directions

Z Sun, Q Guo, J Yang, H Fang, G Guo, J Zhang… - Electronic Commerce …, 2019 - Elsevier
Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

A survey of location prediction on twitter

X Zheng, J Han, A Sun - IEEE Transactions on Knowledge and …, 2018 - ieeexplore.ieee.org
Locations, eg, countries, states, cities, and point-of-interests, are central to news, emergency
events, and people's daily lives. Automatic identification of locations associated with or …

Personalized long-and short-term preference learning for next POI recommendation

Y Wu, K Li, G Zhao, X Qian - IEEE Transactions on Knowledge …, 2020 - ieeexplore.ieee.org
Next POI recommendation has been studied extensively in recent years. The goal is to
recommend next POI for users at specific time given users' historical check-in data …

Causal discovery from temporal data: An overview and new perspectives

C Gong, C Zhang, D Yao, J Bi, W Li, YJ Xu - ACM Computing Surveys, 2024 - dl.acm.org
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …