Abstract
With the development of social media and GPS-enabled devices, people can search for what they are interested in more easily. There are many methods, such as spatial keyword query, proposed to help people get useful information. However, most existing methods are based on location and keywords query which neglect the semantic information. In this paper, we propose a new approach named Top-K Spatio-Topic Query (TKSTQ), which takes semantic information into consideration. We use a topic model to obtain topics of texts and organize index based on topic and location. In this way, the query results can satisfy people’s requirements better. The experimental results on a real dataset validate that our methods can significantly improve the relevance between result and query.
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Acknowledgement
This work is supported by the Natural Science Foundation of China (Grant No. 61532018, 61836007, 61832017).
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Zhou, L., Chen, X., Zhao, Y., Zheng, K. (2019). Top-K Spatio-Topic Query on Social Media Data. 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 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_40
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DOI: https://doi.org/10.1007/978-3-030-18579-4_40
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