时空嵌入式生成对抗网络的地点预测方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201801005

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Abstract

The wide use of positioning technology makes the mining of the people movements easy and plenty of trajectory data are recorded. How to efficiently handle these data for location prediction is a popular research topic as it is fundamental to location-based services(LBS). The existing methods focus either on long time (days or months) visit prediction(i.e. point of interest recommendation) or on real time location prediction(i.e. trajectory prediction). In this paper, the location prediction problem in weak real time conditions is discussed to predict users′ movement in next minutes or hours. A spatial-temporal long-short term memory model(ST-LSTM) combining spatial-temporal influence into LSTM model naturally is proposed to mitigate the data sparse problem. Furthermore, following the idea of generative adversarial network(GAN) for seq2seq learning, the ST-GAN model is proposed, and it takes the proposed ST-LSTM as the generator and the proposed spatial-temporal convolutional neural network(ST-CNN) as the discriminator. The minimax game of ST-GAN can produce more real enough data to train a better prediction model. The proposed ST-GAN is evaluated on a real world trajectory dataset and the results demonstrate the effectiveness of the proposed model.

Key words： Location Prediction Spatial-Temporal Embedding Long-Short Term Memory Network Convolutional Neural Network Generative Adversarial Network

Received: 15 September 2017

About author:: KONG Dejiang, Ph.D.candidate. His research interests include deep learning and recommendation systems.TANG Siliang, Ph.D., associate profe-ssor. His research interests include multimedia analysis, text mining and statistical learning.WU FeiCorresponding author, Ph.D., professor. His research interests include multimedia retrieval, sparse representation and machine learning.

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Cite this article:

KONG Dejiang,TANG Siliang,WU Fei. Location Prediction via Generative Adversarial Network with #br# Spatial Temporal Embedding[J]. , 2018, 31(1): 49-60.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201801005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I1/49

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