CASP selects lower-burden two-stage recommender policies by combining doubly robust estimation with a penalty for weak data support and provides theoretical guarantees for conservative selection.
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The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
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CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
CASP selects lower-burden two-stage recommender policies by combining doubly robust estimation with a penalty for weak data support and provides theoretical guarantees for conservative selection.
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Offline Evaluation Measures of Fairness in Recommender Systems
The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.