Learning Individual Moving Preference and Social Interaction for Location Prediction
Location prediction has attracted increasing attention in diverse fields due to its wide applications, such as traffic planning and control, weather forecasting, homeland security, and travel recommendation. Many existing algorithms forecast a user's next location by learning that user's p...
Main Authors: | Ruizhi Wu, Guangchun Luo, Qinli Yang, Junming Shao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8290840/ |
Similar Items
-
A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data
by: Zhongliang Fu, et al.
Published: (2016-09-01) -
Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network
by: Rui Chen, et al.
Published: (2020-02-01) -
Location Prediction in Social Media Based on Tie Strength
by: McGee, Jeffrey A
Published: (2013) -
Mining moving object data
by: Zendulka Jaroslav, et al.
Published: (2012-10-01) -
GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction
by: Griffin, Terry W.
Published: (2012)