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Reverse k-Ranks Query

Summary: R-kRanks: for a given product, return the top-k customers with the highest rank, guaranteeing 100% coverage across hot and niche items. Three new methods (one tree-based, two batch-pruning) with theory and experiments on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10955
Venue
VLDB
Year
2014
Pagerank
5.1503175e-05
Overall Rank
6,222 | 56.72%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
5,826 Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals 2023 VLDB 5.3124507e-05
6,203 Maximum Rank Query 2015 VLDB 5.1590738e-05
8,584 Geometric Approaches for Top-k Queries 2017 VLDB 4.4914121e-05
8,825 Determining the Impact Regions of Competing Options in Preference Space 2017 SIGMOD 4.4415078e-05
8,877 Creating Top Ranking Options in the Continuous Option and Preference Space 2019 VLDB 4.4302563e-05
9,774 On m-Impact Regions and Standing Top-k Influence Problems 2021 SIGMOD 4.2856106e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

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