Right invariant metrics and measures of presortedness
Discrete Applied Mathematics
What do we know about the metropolis algorithm?
Journal of Computer and System Sciences
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
SIAM Journal on Discrete Mathematics
Overview of the second text retrieval conference (TREC-2)
HLT '94 Proceedings of the workshop on Human Language Technology
Proceedings of the 16th international conference on World Wide Web
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Unsupervised rank aggregation with domain-specific expertise
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Content modeling using latent permutations
Journal of Artificial Intelligence Research
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
Weighted consensus multi-document summarization
Information Processing and Management: an International Journal
A flexible generative model for preference aggregation
Proceedings of the 21st international conference on World Wide Web
Learning to rank under multiple annotators
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Unsupervised ensemble learning for mining top-n outliers
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Learning to rank by aggregating expert preferences
Proceedings of the 21st ACM international conference on Information and knowledge management
Ranking fraud detection for mobile apps: a holistic view
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
CRF framework for supervised preference aggregation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they require domain knowledge or supervised ranked data, both of which are expensive to acquire. In order to address these limitations, we propose a mathematical and algorithmic framework for learning to aggregate (partial) rankings without supervision. We instantiate the framework for the cases of combining permutations and combining top-k lists, and propose a novel metric for the latter. Experiments in both scenarios demonstrate the effectiveness of the proposed formalism.