Matrix analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
SIAM Journal on Discrete Mathematics
Label ranking by learning pairwise preferences
Artificial Intelligence
Computing distances between partial rankings
Information Processing Letters
IEEE Transactions on Information Theory
Clustering Algorithms for Chains
The Journal of Machine Learning Research
Clustering rankings in the fourier domain
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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The goal of this paper is threefold. It first describes a novel way of measuring disagreement between rankings of a finite set χ of n ≥ 1 elements, that can be viewed as a (mass transportation) Kantorovich metric, once the collection rankings of χ is embedded in the set κn of n× n doubly-stochastic matrices. It also shows that such an embedding makes it possible to define a natural notion of median, that can be interpreted in a probabilistic fashion. In addition, from a computational perspective, the convexification induced by this approach makes median computation more tractable, in contrast to the standard metric-based method that generally yields NP-hard optimization problems. As an illustration, this novel methodology is applied to the issue of ranking aggregation, and is shown to compete with state of the art techniques.