ShaRP: Explaining Rankings and Preferences with Shapley Values
Summary: ShaRP adapts Shapley explanations to rankings, attributing features to rank, top-k membership, and pairwise preferences—outcomes for which conventional SHAP is inadequate. It offers scalable support for score-based and learning-to-rank models, with a dedicated evaluation methodology. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Venetia Pliatsika (New York University)
- 2. Joao Fonseca (New York University)
- 3. Kateryna Akhynko (Ukrainian Catholic University)
- 4. Ivan Shevchenko (Ukrainian Catholic University)
- 5. Julia Stoyanovich (New York University)
BibTeX Citation
@article{pliatsika_vldb25,
title = {{ShaRP: Explaining Rankings and Preferences with Shapley Values}},
author = {Pliatsika, Venetia and Fonseca, Joao and Akhynko, Kateryna and Shevchenko, Ivan and Stoyanovich, Julia},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4131--4143},
doi = {10.14778/3749646.3749682},
url = {https://doi.org/10.14778/3749646.3749682},
year = {2025}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,577 | A Nutritional Label for Rankings | 2018 | SIGMOD | 5.8364579e-05 |
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