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Reconciling Real Scores with Binary Comparisons: A New Logistic Based Model for Ranking
Neural Information Processing Systems
The problem of ranking arises ubiquitously in almost every aspect of life, and in particular in Machine Learning/Information Retrieval. A statistical model for ranking predicts how humans rank subsets V of some universe U . In this work we define a statistical model for ranking that satisfies certain desirable properties. The model automatically gives rise to a logistic regression based approach to learning how to rank, for which the score and comparison based approaches are dual views. Thisdblp:conf/nips/Ailon08 fatcat:n6b44sz2pzf5jnkwykqpag37ma