Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening

ICML 2017 Mohsen Ahmadi FahandarEyke HüllermeierInés Couso

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete one, which we call the coarsening process... (read more)

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