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Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries

Summary: Evaluates spatial keyword queries for geo-textual objects; TkQ merges spatial proximity and text relevance, shown effective. DrW combines neural local-interaction text relevance with a query-dependent balance of proximity, outperforming SOTA. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6576
Venue
SIGMOD
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,378 | 21.94%
DOI
10.1145/3588691

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BibTeX Citation

@inproceedings{liu_sigmod23,
        title = {{Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries}},
        author = {Liu, Shang and Cong, Gao and Feng, Kaiyu and Gu, Wanli and Zhang, Fuzheng},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588691},
        url = {https://dl.acm.org/doi/10.1145/3588691},
        year = {2023}
}

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