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Proportionality in Spatial Keyword Search

Summary: Proportional representation for spatial keyword search, integrating location with contextual signals (textual context, tags). Fast algorithms cut pairwise cost of proportional selection; experiments and a user study favor proportional over random or diversification. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6261
Venue
SIGMOD
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,676 | 19.90%
DOI
10.1145/3448016.3457309

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{kalamatianos_sigmod21,
        title = {{Proportionality in Spatial Keyword Search}},
        author = {Kalamatianos, Georgios and Fakas, Georgios J. and Mamoulis, Nikos},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457309},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457309},
        year = {2021}
}

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Rank Citing Paper Year Venue Pagerank
10,244 FIRAS: A Framework for Interval Range Search and Sampling 2026 SIGMOD 5.093636e-05
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