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Continuous spatial keyword search with query result diversifications

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Abstract

With the proliferation of location-based social media, it is of great importance to provide web users and mobile users with timely and high-quality information. In this light, we study the problem of continuous spatial keyword search over a stream of spatio-temporal messages by taking spatial relevance, textual relevance, and result diversification into consideration. We define a novel continuous query named Diversified Continuous Spatial Keyword (DCSK) query. A DCSK query consists of a spatial region, a set of query keywords, a similarity threshold 𝜃, and the number of query results k. Given a stream of spatio-temporal messages, the DCSK query continuously receive spatio-temporal messages such that: (1) they are located inside the query region and contain at least one query keyword, and (2) the similarities between each message and its previous k messages are all mess than 𝜃. Compared to traditional continuous spatial keyword query, the DCSK query can provide subscribers with spatio-temporal messages of higher quality because that the DCSK query takes both spatio-temporal relevance and query result diversification into consideration. We develop a Spatio-temporal Diversified Publish/Subscribe (STD-PS) framework to process a large number of DCSK queries efficiently. We conduct extensive experiments with real-world datasets. Our experimental results confirm the capability of our proposal in terms of result diversity, efficiency, and salability.

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Acknowledgements

This work was supported in part by the Science and Technology Planning Project of Fujian Province (No. 2020H0023, 2020Y9064).

Funding

This work was supported in part by the Science and Technology Planning Project of Fujian Province (No. 2020H0023, 2020Y9064).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

    Ying Zhong, Jianmin Li & Shunzhi Zhu

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  1. Ying Zhong
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  2. Jianmin Li
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  3. Shunzhi Zhu
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Contributions

Ying Zhong: Algorithm development and paper writing. Jianmin Li: Experimental study. Shunzhi Zhu: Algorithm design and paper proofreading. All authors reviewed the manuscript.

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Correspondence to Shunzhi Zhu.

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This article belongs to the Topical Collection: Special Issue on Spatiotemporal Data Management and Analytics for Recommend

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Zhong, Y., Li, J. & Zhu, S. Continuous spatial keyword search with query result diversifications. World Wide Web 26, 1935–1948 (2023). https://doi.org/10.1007/s11280-022-01118-y

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