Abstract
Categories of Point of Interest (POI) facilitate location-based services from many aspects like location search and place recommendation [6]. However, POI categories are often incomplete and new POIs are increasing, this rises the problem of automatic POI classification. Current POI classification methods suffer from two problems: lack of textual information about POIs and not leveraging the hierarchical structure of the categories. In this paper, we propose an Ensemble POI Hierarchical Classification framework (EHC) consisting of three components: Textual and Geographic Feature Extraction, Hierarchical Classifier, and Soft Voting Ensemble Model. We conduct extensive experiments to demonstrate the effectiveness of our framework.
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Acknowledgments
This work is supported by the Key-Area Research and Development Program of Guangdong Province (2019B010153002), NSFC Key Projects (U1736204, 61533018), grants from Beijing Academy of Artificial Intelligence (BAAI2019ZD0502) and Institute for Guo Qiang, Tsinghua University (2019GQB0003), and THUNUS NExT Co-Lab.
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Liu, S., Yu, J., Li, J., Hou, L. (2020). Geographical Information Enhanced POI Hierarchical Classification. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_10
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DOI: https://doi.org/10.1007/978-3-030-60029-7_10
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