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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)

Paper ID
10579
Venue
VLDB
Year
2013
Pagerank
4.1945683e-05
Overall Rank
12,067 | 16.06%
DOI
-

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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 4.4417735e-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
1,403 Efficient Processing of Top-k Spatial Preference Queries 2011 VLDB 0.00012176993
4,786 Collective Spatial Keyword Querying 2011 SIGMOD 5.9235651e-05
13,472 SWORS: A System for the Efficient Retrieval of Relevant Spatial Web Objects 2012 VLDB -
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