Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals
Summary: Defines 'why-not-yet' for top-k under linear scalarization: find weight vectors that would admit a target into top-k, casting it as satisfiability/optimization with potentially quantified, disjunctive, or negated constraints. Introduces a monotonic-core linear approximation and tunable algorithms to trade runtime for solution quality, demonstrating scalable empirical gains over the state of the art. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zixuan Chen (Northeastern University)
- 2. Panagiotis Manolios (Northeastern University)
- 3. Mirek Riedewald (Northeastern University)
BibTeX Citation
@article{chen_vldb23,
title = {{Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals}},
author = {Chen, Zixuan and Manolios, Panagiotis and Riedewald, Mirek},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {9},
pages = {2377--2390},
doi = {10.14778/3598581.3598606},
url = {https://doi.org/10.14778/3598581.3598606},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,520 | Query Refinement for Diverse Top-k Selection | 2024 | SIGMOD | 5.7573717e-05 |
| 9,480 | Ranking Indicator Discovery from Very Large Knowledge Graphs | 2025 | VLDB | 5.1708619e-05 |
| 10,476 | Local Stability of Rankings | 2026 | SIGMOD | 4.9793485e-05 |
| 10,739 | Database Views as Explanations for Relational Deep Learning | 2026 | VLDB | 4.9793485e-05 |
| 11,405 | GooseDB: A Database Engine that Optimally Refines Top-k Queries to Satisfy Representation Constraints | 2025 | VLDB | 4.9793485e-05 |
| 11,609 | Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs | 2024 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 26 of 26 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
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| 1 | 11,733 | Equitable Top-k Results for Long Tail Data | 2023 | SIGMOD |
| 2 | 7,327 | Anytime Measures for Top-k Algorithms | 2007 | VLDB |
| 3 | 3,105 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD |
| 4 | 6,520 | Query Refinement for Diverse Top-k Selection | 2024 | SIGMOD |
| 5 | 9,397 | Satisfying Complex Top-k Fairness Constraints by Preference Substitutions | 2023 | VLDB |
| 6 | 6,176 | Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings | 2018 | VLDB |
| 7 | 8,062 | Evaluating Top-k Queries with Inconsistency Degrees | 2020 | VLDB |
| 8 | 2,620 | Ranking with Uncertain Scoring Functions: Semantics and Sensitivity Measures | 2011 | SIGMOD |
| 9 | 1,379 | Designing Fair Ranking Schemes | 2019 | SIGMOD |
| 10 | 2,955 | Answering Why-not Questions on Reverse Top-k Queries | 2015 | VLDB |