Abstract
Spatial keyword queries are widely used in location based service systems nowadays to find the spatial web object people need. The returned objects usually are relevant to the query but not diversified each other. Motivated by this, we study the problem of diversified spatial keyword query on topic coverage, which returns k relevant objects close to the query, and they together can cover a certain number of topics for the purpose of diversification. We devise two novel algorithms, one aims to iteratively include the objects with minimum marginal penalty on top of a carefully designed indexing structure; the other adopts a hierarchy based selection policy, and its effectiveness can be confirmed by an error bound derived through the theoretical analysis. Empirical study based on real check-in dataset demonstrate the good effectiveness and efficiency of our proposed algorithms.
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Qian, Z., Zhang, L., Zhu, H., Xu, J. (2018). Diversified Spatial Keyword Query on Topic Coverage. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_3
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DOI: https://doi.org/10.1007/978-3-030-01298-4_3
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