Abstract
On most location-based social applications today, users are strongly encouraged to share activities by checking-in. In this way, vast amounts of user-generated data can be accumulated, which include spatial and temporal information. Much research has been conducted on these data, which enables heightening the understanding of human mobility. Therefore, the next location problem has attracted significant attention and has been extensively studied. In this paper, we propose a next location prediction approach based on a recurrent neural network and self-attention mechanism. Our model can explore sequence regularity and extract temporal feature according to historical trajectories information. We conduct our experiments on the location-based social network (LBSN) dataset, and the results indicate the effectiveness of our model when compared with the other three frequently-used methods.
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Acknowledgment
This research is supported by the National Natural Science Foundation of China (Grant No. 61502062, Grant No. 61672117 and Grant No. 61602070), the China Postdoctoral Science Foundation under Grant 2014M560704, the Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry), and the Fundamental Research Funds for the Central Universities Project No. 2015CDJXY.
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Zeng, J., He, X., Tang, H., Wen, J. (2019). A Next Location Predicting Approach Based on a Recurrent Neural Network and Self-attention. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_21
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DOI: https://doi.org/10.1007/978-3-030-30146-0_21
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