[1]CHAI Ruimin,YIN Chen,MENG Xiangfu,et al.A recurrent neural network model based on spatial and temporal information for the next point of interest recommendation[J].CAAI Transactions on Intelligent Systems,2021,16(3):407-415.[doi:10.11992/tis.202004009]
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A recurrent neural network model based on spatial and temporal information for the next point of interest recommendation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 407-415 Column: 学术论文—机器学习 Public date: 2021-05-05
Title:
A recurrent neural network model based on spatial and temporal information for the next point of interest recommendation
Author(s):
CHAI Ruimin;  YIN Chen;  MENG Xiangfu;  ZHANG Xiaoyan;  GUAN Xin;  QI Xueyue
School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
Keywords:
next point of interest recommendation; location-based social networks; recurrent neural network; sequence information; temporal preferences; spatial preferences; user preferences; session
CLC:
TP183
DOI:
10.11992/tis.202004009
Abstract:
The next point-of-interest (POI) recommendation has become an important task in location-based social networks. The existing models lack in-depth research on the temporal and spatial information transition between adjacent check-in POIs and cannot effectively model the long/short time and distance preferences of the users accessing the next POI. In response, this paper proposes a new session-based spatial–temporal recurrent neural network (SST-RNN) model that is used to recommend the next POI. This model takes advantage of the spatial transition matrix and temporal transition matrix to respectively model the user’s spatial and temporal preferences, and comprehensively considers the sequence information and spatial–temporal information of consecutive check-in POIs as well as user preferences to do the next POI recommendation. Experimental results in two real open datasets show that the performance of the proposed SST-RNN model is significantly enhanced compared with the state-of-the-art models. On the Foursquare and CA datasets, the ACC@5 is increased by 36.38% and 13.81%, and the MAP is increased by 30.72% and 17.26%, respectively.
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