Abstract
Personalized points of interests (POI) recommendation is an important basis for location-based services. A typical application scenario is to recommend a region with reliable POIs to a user when he/she travels to an unfamiliar area without any background knowledge. In this study, we explore spatiotemporal-aware region recommendation to manage this learning task. We propose a unified deep learning model that comprehensively incorporates dynamic personal and global user preferences across regions, along with spatiotemporal dependencies, into check-in region history. We model and fuse user preferences through a pyramidal ConvLSTM component, and capture the dynamic region attributes through a recurrent component. Two components are seamlessly assembled in a unified framework to yield next time region recommendation. Extensive experiments on real-word datasets demonstrate the effectiveness of the proposed model.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61772288, U1636116, 11431006, the Natural Science Foundation of Tianjin City under Grant No. 18JCZDJC30900, the Research Fund for International Young Scientists under Grant No. 61750110530, and the Ministry of education of Humanities and Social Science project under grant 16YJC790123.
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Xu, H., Zhang, Y., Wei, J., Yang, Z., Wang, J. (2019). Spatiotemporal-Aware Region Recommendation with Deep Metric Learning. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_73
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DOI: https://doi.org/10.1007/978-3-030-18590-9_73
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