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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References
Bao, J., Lian, D., Zhang, F., Yuan, N.J.: Geo-social media data analytic for user modeling and location-based services. SIGSPATIAL Spec. 3, 11–18 (2016)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chen, L., Lin, X., Hu, H., Jensen, C.S., Xu, J.: Answering why-not questions on spatial keyword top-k queries. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 279–290 (2015)
Chen, L., Xu, J., Lin, X., Jensen, C.S., Hu, H.: Answering why-not spatial keyword top-k queries via keyword adaption. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 697–708 (2016)
Chen, Q., Yao, L., Yang, J.: Short text classification based on LDA topic model. In: 2016 International Conference on Audio, Language and Image Processing (ICALIP), pp. 749–753 (2016)
Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 656–665 (2008)
Iijima, R., Kamada, Y.: Social distance and network structures. Theor. Econ. 2, 655–689 (2017)
Jiang, L., Lu, H., Xu, M., Wang, C.: Biterm pseudo document topic model for short text. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 865–872 (2016)
Lee, K., Ganti, R.K., Srivatsa, M., Liu, L.: When Twitter meets foursquare: Tweet location prediction using foursquare. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 198–207 (2014)
Li, Z., Lee, K.C.K., Zheng, B., Lee, W.C., Lee, D., Wang, X.: IR-tree: an efficient index for geographic document search. IEEE Trans. Knowl. Data Eng. 4, 585–599 (2011)
Lim, K.W., Chen, C., Buntine, W.L.: Twitter-network topic model: a full Bayesian treatment for social network and text modeling. CoRR (2016)
Nguyen, D.Q., Billingsley, R., Du, L., Johnson, M.: Improving topic models with latent feature word representations. Trans. Assoc. Comput. Linguist. 3, 299–313 (2015)
Pontes, T., et al.: Beware of what you share: inferring home location in social networks. In: 2012 IEEE 12th International Conference on Data Mining Workshops, pp. 571–578 (2012)
Ray, S., Nickerson, B.G.: Dynamically ranked top-k spatial keyword search. In: Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, pp. 6:1–6:6 (2016)
Skovsgaard, A., Sidlauskas, D., Jensen, C.S.: Scalable top-k spatio-temporal term querying. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 148–159 (2014)
Tang, J., Musolesi, M., Mascolo, C., Latora, V.: Temporal distance metrics for social network analysis. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 31–36 (2009)
Wang, D., Li, Z., Salamatian, K., Xie, G.: The pattern of information diffusion in microblog. In: Proceedings of the ACM CoNEXT Student Workshop, pp. 3:1–3:2 (2011)
Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: efficient top k spatial keyword search. IEEE Trans. Knowl. Data Eng. 7, 1706–1721 (2016)
Zhang, J., Liu, D., Meng, X.: Preference-based top-k spatial keyword queries. In: Proceedings of the 1st International Workshop on Mobile Location-based Service, pp. 31–40 (2011)
Zhao, H., Du, L., Buntine, W.: A word embeddings informed focused topic model. In: Zhang, M.L., Noh, Y.K. (eds.) Proceedings of the Ninth Asian Conference on Machine Learning, pp. 423–438 (2017)
Zhao, H., Du, L., Buntine, W.L., Liu, G.: MetaLDA: a topic model that efficiently incorporates meta information. CoRR (2017)
Zuo, Y., et al.: Topic modeling of short texts: a pseudo-document view. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2105–2114 (2016)
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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