GroupFinder: A New Approach to Top-K Point-of-Interest Group Retrieval
Summary: GroupFinder reframes PoI search as top-k group retrieval, returning nearby PoI sets that jointly match keywords and location. Shows practical efficiency under combinatorial limits by parameterizing options into user-relevant choices. (summarized by gpt-5-nano on Feb 09 2026)
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,822 | Querying Geo-Textual Data: Spatial Keyword Queries and Beyond | 2016 | SIGMOD | 4.437516e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,317 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB | 0.00012615781 |
| 4,693 | Collective Spatial Keyword Querying | 2011 | SIGMOD | 5.9867895e-05 |
| 13,485 | SWORS: A System for the Efficient Retrieval of Relevant Spatial Web Objects | 2012 | VLDB | - |
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