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Determining the Impact Regions of Competing Options in Preference Space

Summary: Introduces k-Shortlist Preference Region (kSPR) to identify regions in the d-dimensional preference space where a focal record ranks in the top-k under weighted-sum scoring. Proposes a computational-geometry, exact framework for efficient kSPR computation; benchmarks show up to three orders of magnitude speedup over a prior competitor built from earlier work. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5420
Venue
SIGMOD
Year
2017
Pagerank
4.4415078e-05
Overall Rank
8,825 | 38.61%
DOI
10.1145/3035918.3064044

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