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Predicting the next location: A self‐attention and recurrent neural network model with temporal context
Author(s) -
Zeng Jun,
He Xin,
Tang Haoran,
Wen Junhao
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3898
Subject(s) - computer science , popularity , context (archaeology) , location based service , location data , recurrent neural network , feature (linguistics) , artificial neural network , spatial contextual awareness , artificial intelligence , data mining , mobile device , machine learning , geography , real time computing , world wide web , psychology , archaeology , social psychology , telecommunications , linguistics , philosophy
Abstract Nowadays, the popularity of mobile devices and location‐based services have generated a large amount of geographic data. It provides the opportunity for researchers to employ techniques to predict the next location. However, predicting the next location is difficult because it depends on temporal and spatial factors, and it is closely related to the historical behavior of users. In this article, we first analyze the geographic data of users and discover the potential behavior patterns of users. Then, we mine the relationship between user's movement behavior and temporal feature. Hence, we propose a method based on a recurrent neural network and self‐attention mechanism to predict the next location where users may visit. Our model can explore sequence regularity and extract temporal features according to historical trajectories information. Experimental results on a real‐world dataset demonstrate the effectiveness of our proposed model.