GeoBloom: Revisiting Lightweight Models for Geographic Information Retrieval
Summary: GeoBloom encodes documents/queries as fine-grained Bloom filters and uses intersecting bits as a robust unsupervised text-similarity signal, outperforming PLM-based GIR with few or no labeled queries. A Bloom Filter Evaluator (label-efficient), sparsity-aware search and a tree-based index deliver massive speed, memory, and disk reductions versus PLMs. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |
| 648 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 0.00018666267 |
| 1,220 | Efficient Query Processing in Geographic Web Search Engines | 2006 | SIGMOD | 0.00013223504 |
| 1,403 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB | 0.00012176993 |
| 11,172 | Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries | 2023 | SIGMOD | 4.1945683e-05 |
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