Generalized distances between rankings

337 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019, background): “Other possibilities involve using logarithms rather than hyperbolic functions as in the discounted cumulative gain (Kumar & Vassilvitskii, 2010).”
Ranking with Fairness Constraints (2017, background): “One can also think of weighted versions of these metrics, as introduced in [KV10], for which these observations generalize.”
Measuring Price Discrimination and Steering on E-commerce Web Sites (2014, background): “Several studies improve on Kendall’s τ by adding per-rank weights [14,40], and by taking item similarity into account [21,39].”