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)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 54 | DISCOVER: Keyword Search in Relational Databases | 2002 | VLDB | 0.00066047203 |
| 276 | Efficient IR-Style Keyword Search over Relational Databases | 2003 | VLDB | 0.00029336949 |
| 648 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 0.00018666267 |
| 7,265 | Efficient Computation of Top-k Frequent Terms over Spatio-temporal Ranges | 2017 | SIGMOD | 4.7834983e-05 |
| 7,954 | Diverse and Proportional Size-l Object Summaries for Keyword Search | 2015 | SIGMOD | 4.613363e-05 |
| 9,183 | Size-l Object Summaries for Relational Keyword Search | 2012 | VLDB | 4.3799925e-05 |
| 9,898 | Top-k Relevant Semantic Place Retrieval on Spatial RDF Data | 2016 | SIGMOD | 4.2600049e-05 |
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