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ShaRP: Explaining Rankings and Preferences with Shapley Values

Summary: ShaRP extends Shapley-value explanations to ranking by defining rank- and top-k–aware contribution scores and a novel Shapley-based pairwise preference attribution. Efficient implementation for score-based and learning-to-rank models plus comprehensive evaluations show scalable, complementary insights. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14032
Venue
VLDB
Year
2025
Pagerank
-
Overall Rank
13,119 | 8.74%
DOI
10.14778/3749646.3749682

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Rank Cited Paper Year Venue Pagerank
7,191 A Nutritional Label for Rankings 2018 SIGMOD 4.8049534e-05
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