{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:43:34Z","timestamp":1772909014742,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisions on individuals, organizations, and population groups, it is essential to understand them\u2014to help individuals improve their ranking position, design better ranking procedures, and ensure legal compliance. In this paper, we argue that explainability methods for classification and regression, such as SHAP, are insufficient for ranking tasks, and present ShaRP\u2014Shapley Values for Rankings and Preferences\u2014a framework that explains the contributions of features to various aspects of a ranked outcome.<\/jats:p>\n          <jats:p>\n            ShaRP computes feature contributions for various ranking-specific profit functions, such as rank and top-\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            , and also includes a novel Shapley value-based method for explaining pairwise preference outcomes. We provide a flexible implementation of ShaRP, capable of efficiently and comprehensively explaining ranked and pairwise outcomes over tabular data, in score-based ranking and learning-to-rank tasks. Finally, we develop a comprehensive evaluation methodology for ranking explainability methods, showing through qualitative, quantitative, and usability studies that our rank-aware QoIs offer complementary insights, scale effectively, and help users interpret ranked outcomes in practice.\n          <\/jats:p>","DOI":"10.14778\/3749646.3749682","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T17:55:06Z","timestamp":1757008506000},"page":"4131-4143","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["ShaRP: Explaining Rankings and Preferences with Shapley Values"],"prefix":"10.14778","volume":"18","author":[{"given":"Venetia","family":"Pliatsika","sequence":"first","affiliation":[{"name":"New York University, New York, NY, USA"}]},{"given":"Joao","family":"Fonseca","sequence":"additional","affiliation":[{"name":"New York University, New York, NY, USA"}]},{"given":"Kateryna","family":"Akhynko","sequence":"additional","affiliation":[{"name":"Ukrainian Catholic University, Lviv, Ukraine"}]},{"given":"Ivan","family":"Shevchenko","sequence":"additional","affiliation":[{"name":"Ukrainian Catholic University, Lviv, Ukraine"}]},{"given":"Julia","family":"Stoyanovich","sequence":"additional","affiliation":[{"name":"New York University, New York, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE55066.2022.9882743"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3159245"},{"key":"e_1_2_1_3_1","unstructured":"Emery Berger. 2023. 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API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108\u2013122."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00657-x"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3578337.3605138"},{"key":"e_1_2_1_8_1","unstructured":"Tanya Chowdhury Yair Zick and James Allan. 2024. RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank. arXiv:2405.01848 [cs.IR] https:\/\/arxiv.org\/abs\/2405.01848"},{"key":"e_1_2_1_9_1","unstructured":"Ian Covert Scott M. Lundberg and Su-In Lee. 2020. Understanding Global Feature Contributions With Additive Importance Measures. 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Retiring Adult: New Datasets for Fair Machine Learning. http:\/\/arxiv.org\/abs\/2108.04884 arXiv:2108.04884 [cs, stat]."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331312"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3436905.3436922"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"e_1_2_1_16_1","unstructured":"Maria Heuss Maarten de Rijke and Avishek Anand. 2024. Ranking-SHAP - Listwise Feature Attribution Explanations for Ranking Models. arXiv:2403.16085 [cs.IR] https:\/\/arxiv.org\/abs\/2403.16085"},{"key":"e_1_2_1_17_1","volume-title":"Jaime Ferrando Huertas, and Dino Sejdinovic.","author":"Hu Robert","year":"2022","unstructured":"Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, and Dino Sejdinovic. 2022. Explaining Preferences with Shapley Values. 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