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Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
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bradley-terry-rankings-for-recommender-systems-across-dataset-taxonomies-ba4cd94d·1 events·first seen 9d agoAliases: Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
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Bradley-Terry model proposed for fairer ranking of recommendation algorithms across dataset types
A new arXiv preprint introduces a Bradley-Terry (BT) model-based methodology for ranking recommendation algorithms in a way that accounts for dataset characteristics such as sparsity, sequential structure, and scale. The authors argue that naive metric aggregation (e.g., averaging NDCG) produces misleading rankings and propose BT trees and covariate-extended BT models as alternatives. The framework also enables ranking predictions on unseen datasets without running the models, and includes a new metric for ranking consistency.