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Geometric Approaches for Top-k Queries

Summary: Geometric framing of top-k queries; geometric variants and practical extensions via computational geometry tools. Examines dimensionality effects on meaningfulness; parallels to nearest-neighbor search with implications for recsys and decision-support. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11503
Venue
VLDB
Year
2017
Pagerank
4.4914121e-05
Overall Rank
8,584 | 40.29%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,555 On Obtaining Stable Rankings 2019 VLDB 5.4386174e-05
5,855 Optimal Join Algorithms Meet Top-k 2020 SIGMOD 5.3006096e-05
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Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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