Rank Aggregation and Prediction with Item Features


We study the problem of rank aggregation with features, where both pairwise comparisons and item features are available to help the rank aggregation task. Observing that traditional rank aggregation methods disregard features, while models adapted from learning-to-rank task are sensitive to feature noise, we propose a general model to learn a total ranking… (More)


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