Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Order SVM: a kernel method for order learning based on generalized order statistics
Systems and Computers in Japan
Journal of Artificial Intelligence Research
Vote elicitation with probabilistic preference models: empirical estimation and cost tradeoffs
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
TreeMatrix: A Hybrid Visualization of Compound Graphs
Computer Graphics Forum
Robust approximation and incremental elicitation in voting protocols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
Multi-winner social choice with incomplete preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results or bestseller lists. In spite of their importance, methods of processing orders have received little attention. However, research concerning orders has recently become common; in particular, researchers have developed various methods for the task of Supervised Ordering to acquire functions for object sorting from example orders. Here, we give a unified view of these methods and our new one, and empirically survey their merits and demerits.