Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregation of partial rankings, p-ratings and top-m lists
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Label ranking by learning pairwise preferences
Artificial Intelligence
Improved bounds for computing Kemeny rankings
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Preference functions that score rankings and maximum likelihood estimation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Optimal social choice functions: a utilitarian view
Proceedings of the 13th ACM Conference on Electronic Commerce
Efficient vote elicitation under candidate uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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One of the fundamental problems in the theory of social choice is aggregating the rankings of a set of agents (or voters) into a consensus ranking. Rank aggregation has found application in a variety of computational contexts. However, the goal of constructing a consensus ranking rather than, say, a single outcome (or winner) is often left unjustified, calling into question the suitability of classical rank aggregation methods. We introduce a novel model which offers a decision-theoretic motivation for constructing a consensus ranking. Our unavailable candidate model assumes that a consensus choice must be made, but that candidates may become unavailable after voters express their preferences. Roughly speaking, a consensus ranking serves as a compact, easily communicable representation of a decision policy that can be used to make choices in the face of uncertain candidate availability. We use this model to define a principled aggregation method that minimizes expected voter dissatisfaction with the chosen candidate. We give exact and approximation algorithms for computing optimal rankings and provide computational evidence for the effectiveness of a simple greedy scheme. We also describe strong connections to popular voting protocols such as the plurality rule and the Kemeny consensus, showing specifically that Kemeny produces optimal rankings in the unavailable candidate model under certain conditions.