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Fairness in Preference Queries: Social Choice Theories Meet Data Management

Summary: Applies social choice methods to preference queries at scale, comparing elicitation models and aggregators (Kemeny, Borda, STV/IRV) and outputs (full orders, top‑k). Surveys fairness fixes—input/output repairs and metrics—and pinpoints scalability, elicitation‑aware fairness, and algorithmic/complexity gaps for future DB research. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13622
Venue
VLDB
Year
2024
Pagerank
4.3690661e-05
Overall Rank
9,241 | 35.72%
DOI
10.14778/3685800.3685841

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,223 On Fair Epsilon Net and Geometric Hitting Set 2026 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
3,802 Group Recommendation: Semantics and Efficiency 2009 VLDB 6.7552492e-05
6,427 From Group Recommendations to Group Formation 2015 SIGMOD 5.0670573e-05
7,632 Rank Aggregation with Proportionate Fairness 2022 SIGMOD 4.6915165e-05
8,972 Satisfying Complex Top-k Fairness Constraints by Preference Substitutions 2023 VLDB 4.4187185e-05
11,218 Equitable Top-k Results for Long Tail Data 2023 SIGMOD 4.1945683e-05
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