GeoBloom: Revisiting Lightweight Models for Geographic Information Retrieval
Summary: GeoBloom replaces PLMs for GIR with fine-grained Bloom-filter text encoding, an unsupervised intersecting-bit similarity metric, and a learned evaluator. Its sparse tree index delivers superior NDCG with few/no labels while dramatically reducing latency, memory, and disk. (summarized by gpt-5.6-luna on Jul 24 2026)
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BibTeX Citation
@article{li_vldb25,
title = {{GeoBloom: Revisiting Lightweight Models for Geographic Information Retrieval}},
author = {Li, Yi and Cong, Gao},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {5},
pages = {1348--1361},
doi = {10.14778/3718057.3718064},
url = {https://doi.org/10.14778/3718057.3718064},
year = {2025}
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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 |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 1,212 | Efficient Query Processing in Geographic Web Search Engines | 2006 | SIGMOD | 0.00011647465 |
| 1,441 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB | 0.00010778831 |
| 1,819 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 9.6810907e-05 |
| 11,378 | Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries | 2023 | SIGMOD | 5.093636e-05 |
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