Location Prediction Model Based on ST-LSTM Network

doi:10.19678/j.issn.1000-3428.0052883

Computer Engineering ›› 2019, Vol. 45 ›› Issue (9): 1-7. doi: 10.19678/j.issn.1000-3428.0052883

Previous Articles Next Articles

Location Prediction Model Based on ST-LSTM Network

XU Fangfang¹, YANG Junjie², LIU Hongzhi¹

1. School of Software and Microelectronic, Peking University, Beijing 102600, China;
2. State Grid Zhongxing Co., Ltd., Beijing 100761, China

Received:2018-10-16 Revised:2018-12-16 Online:2019-09-15 Published:2019-09-03
Supported by:
This work is supported by the National Science and Technology Support Program (No.2015BAA05B01) and the National Key R&D Program of China (No.2017YFC0210203).

基于ST-LSTM网络的位置预测模型

许芳芳¹, 杨俊杰², 刘宏志¹

1. 北京大学软件与微电子学院, 北京 102600;
2. 国网中兴有限公司, 北京 100761

作者简介:许芳芳(1992-),女,硕士研究生,主研方向为数据挖掘、深度学习;杨俊杰,硕士;刘宏志,副教授、博士。
基金资助:
国家重点研发计划（2017YFB1002000）。

Abstract

Abstract: Existing research on location prediction usually neglects the correlation between time and space.To address the problem,this paper proposes a Long and Short Term Memory model based on Spatial and Temporal features(ST-LSTM).Based on the LSTM network,a spatial-temporal gate that independently processes the spatial-temporal information of the user's move is added,and the spatial and temporal factors of the user's sign-in are added,so that the model has spatial-temporal characteristics.The personal correction factor is introduced in the ST-LSTM network to correct the output of each type of users,highlighting the individuality on the basis of ensuring the basic characteristics,and better learning the behavioral trajectory characteristics of each type of users.At the same time,a simplified variant model of two ST-LSTM networks is proposed under the premise of ensuring the characteristics of the ST-LSTM network.Results of testing on the public dataset show that compared with the mainstream location prediction methods,the prediction model has better performance in terms of accuracy,recall rate,and F1 value.

Key words: location prediction, Long Short Term Memory(LSTM) model, spatial-temporal information, individuality, behavioral trajectory

摘要： 针对现有位置预测研究多数忽略时间和空间之间关联性的问题，提出一种基于时空特性的长短期记忆模型（ST-LSTM）。基于LSTM网络添加单独处理用户移动行为时空信息的时空门，并考虑用户签到的时间及空间因素，从而使模型具有时空特性。在ST-LSTM网络中引入个人修正因子，对每类用户的输出结果进行修正，在确保基本特性的基础上突出个性化，更好地学习每类用户的行为轨迹特征，同时在保证ST-LSTM网络特性的前提下给出2种ST-LSTM网络的简化变体模型。在公开数据集上的测试结果表明，与主流位置预测方法相比，该预测模型精确率、召回率、F1值都有明显提升。

关键词: 位置预测, 长短期记忆模型, 时空信息, 个性化, 行为轨迹

CLC Number:

TP393

XU Fangfang, YANG Junjie, LIU Hongzhi. Location Prediction Model Based on ST-LSTM Network[J]. Computer Engineering, 2019, 45(9): 1-7.

许芳芳, 杨俊杰, 刘宏志. 基于ST-LSTM网络的位置预测模型[J]. 计算机工程, 2019, 45(9): 1-7.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0052883

http://www.ecice06.com/EN/Y2019/V45/I9/1

Figures/Tables 11

References

[1] KOLODZIEJ K W,HJELM J.Local positioning systems:LBS applications and services[M].[S.1.]:CRC Press,2006:101-158.
[2] ASAHARA A,MARUYAMA K,SATO A,et al.Pedestrian movement prediction based on mixed Markov-chain model[C]//Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York,USA:ACM Press,2011:25-33.
[3] QIAO Shaojie,SHEN Dayong,WANG Xiaoteng,et al.A self-adaptive parameter selection trajectory prediction approach via hidden Markov models[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(1):284-296.
[4] LIAN Defu,ZHENG V W,XIE Xing.Collaborative filtering meets next check-in location prediction[C]//Proceedings of the 22nd International Conference on World Wide Web.New York,USA:ACM Press,2013:231-232.
[5] MORZY M.Prediction of moving object location based on frequent trajectories[C]//Proceedings of International Symposium on Computer and Information Sciences.Berlin,Germany:Springer,2006:583-592.
[6] MORZY M.Mining frequent trajectories of moving objects for location prediction[C]//Proceedings of International Workshop on Machine Learning and Data Mining in Pattern Recognition.Berlin,Germany:Springer,2007:667-680.
[7] GAMBS S,KILLIJIAN M O,DEL PRADO C M N.Next place prediction using mobility markov chains[C]//Proceedings of the 1st Workshop on Measurement,Privacy,and Mobility.New York,USA:ACM Press,2012:3.
[8] BAUMANN P,KLEIMINGER W,Santini S.The influence of temporal and spatial featureson the performance of next-place prediction algorithms[C]//Proceedings of ACM International Joint Conference on Pervasive and Ubiquitous Computing.New York,USA:ACM Press,2013:449-458.
[9] YE Jihang,ZHU Zhe,CHENG Hong.What's your next move:user activity prediction in location-based social networks[C]//Proceedings of 2013 SIAM International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2013:171-179.
[10] LIU Qiang,WU Shu,WANG Liang,et al.Predicting the next location:a recurrent model with spatial and temporal contexts[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence.[S.1.]:AAAI Press,2016:194-200.
[11] PASCANU R,MIKOLOV T,BENGIO Y.On the difficulty of training recurrent neural networks[C]//Proceedings of the 30th IEEE International Conference on Machine Learning.Washington D.C.,USA:IEEE Press,2013:1310-1318.
[12] BENGIO Y,SIMARD P,FRASCONI P.Learning long-term dependencies with gradient descent is difficult[J].IEEE Transactions on Neural Networks,2002,5(2):157-166.
[13] WANG Jian.Chinese text sentiment analysis using LSTM network based on L2 and nadam[C]//Proceedings of the 17th International IEEE Conference on Communication Technology.Washington D.C.,USA:IEEE Press,2017:1891-1895.
[14] 梁军,柴玉梅,原慧斌,等.基于极性转移和LSTM递归网络的情感分析[J].中文信息学报,2015,29(5):152-159.
[15] GRAVES A.Supervised sequence labelling with recurrent neural networks[M].Berlin,Germany:Springer,2012.
[16] GRAVES A,SCHMIDHUBER J.Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J].Neural Networks,2005,18(5):602-610.
[17] NNEIL D,PFEIFFER M,LIU S C.Phased LSTM:accelerating recurrent network training for long or event-based sequences[C]//Proceedings of ANIPS'16.Washington D.C.,USA:IEEE Press,2016:3882-3890.
[18] GREFF K,SRIVASTAVA R K,KOUTNIK J,et al.LSTM:a search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(10):2222-2232.

Please choose a citation manager

Content to export