Aggregating inconsistent information: Ranking and clustering
Journal of the ACM (JACM)
Average parameterization and partial kernelization for computing medians
Journal of Computer and System Sciences
The nearest neighbor spearman footrule distance for bucket, interval, and partial orders
FAW-AAIM'11 Proceedings of the 5th joint international frontiers in algorithmics, and 7th international conference on Algorithmic aspects in information and management
Using medians to generate consensus rankings for biological data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Average parameterization and partial kernelization for computing medians
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
Comparing and aggregating partial orders with kendall tau distances
WALCOM'12 Proceedings of the 6th international conference on Algorithms and computation
The feedback arc set problem with triangle inequality is a vertex cover problem
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Campaigns for lazy voters: truncated ballots
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Studies in computational aspects of voting: open problems of downey and fellows
The Multivariate Algorithmic Revolution and Beyond
The nearest neighbor Spearman footrule distance for bucket, interval, and partial orders
Journal of Combinatorial Optimization
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We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-commerce, learning from experts, analysis of population preference sampling, committee decision making and more. We improve recent constant factor approximation algorithms for aggregation of full rankings and generalize them to partial rankings. Our algorithms improve constant factor approximation with respect to a family of metrics recently proposed in the context of comparing partial rankings. We pay special attention to two important types of partial rankings: the well-known top-m lists and the more general p-ratings which we define. We provide first evidence for hardness of aggregating them for constant m, p.