SIAM Review
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Uncovering Organizational Hierarchies
Computational & Mathematical Organization Theory
Ordering patterns by combining opinions from multiple sources
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
SIAM Journal on Discrete Mathematics
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
The power of convex relaxation: near-optimal matrix completion
IEEE Transactions on Information Theory
Statistical ranking and combinatorial Hodge theory
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Recovery of exact sparse representations in the presence of bounded noise
IEEE Transactions on Information Theory
Recovering Low-Rank Matrices From Few Coefficients in Any Basis
IEEE Transactions on Information Theory
Multi-objective ranking of comments on web
Proceedings of the 21st international conference on World Wide Web
A flexible generative model for preference aggregation
Proceedings of the 21st international conference on World Wide Web
Learning to rank by aggregating expert preferences
Proceedings of the 21st ACM international conference on Information and knowledge management
Pairwise ranking aggregation in a crowdsourced setting
Proceedings of the sixth ACM international conference on Web search and data mining
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 process of rank aggregation is intimately intertwined with the structure of skew symmetric matrices. We apply recent advances in the theory and algorithms of matrix completion to skew-symmetric matrices. This combination of ideas produces a new method for ranking a set of items. The essence of our idea is that a rank aggregation describes a partially filled skew-symmetric matrix. We extend an algorithm for matrix completion to handle skew-symmetric data and use that to extract ranks for each item. Our algorithm applies to both pairwise comparison and rating data. Because it is based on matrix completion, it is robust to both noise and incomplete data. We show a formal recovery result for the noiseless case and present a detailed study of the algorithm on synthetic data and Netflix ratings.