ShaRP: Explaining Rankings and Preferences with Shapley Values
Summary: ShaRP extends Shapley-value explanations to ranking by defining rank- and top-kâaware 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)
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| 7,191 | A Nutritional Label for Rankings | 2018 | SIGMOD | 4.8003424e-05 |
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