Deep learning for spatio-temporal data mining: A survey
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 …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
A survey on deep learning for human mobility
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 …
such as disease spreading, urban planning, well-being, pollution, and more. The …
Stan: Spatio-temporal attention network for next location recommendation
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 …
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
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 …
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
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 …
Graph-flashback network for next location recommendation
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 …
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
Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …
overload problem in recent decades. Traditional recommender systems, however, suffer …
A survey of location prediction on twitter
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 …
events, and people's daily lives. Automatic identification of locations associated with or …
Personalized long-and short-term preference learning for next POI recommendation
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 …
recommend next POI for users at specific time given users' historical check-in data …
Causal discovery from temporal data: An overview and new perspectives
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 …
been a typical data structure that can be widely generated by many domains, such as …