Global partial orders from sequential data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Data Analysis and Mining in Ordered Information Tables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Nantonac collaborative filtering: recommendation based on order responses
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Filling-in Missing Objects in Orders
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Journal of Artificial Intelligence Research
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In this paper, we advocate a learning task that deals with the orders of objects, which we call the Supervised Ordering task. The term order means a sequence of objects sorted according to a specific property, such as preference, size, cost. The aim of this task is to acquire the rule that is used for estimating an appropriate order of a given unordered object set. The rule is acquired from sample orders consisting of objects represented by attribute vectors. Developing solution methods for accomplishing this task would be useful, for example, in carrying out a questionnaire survey to predict one's preferences. We develop a solution method based on a regression technique imposing a Thurstone's model and evaluate the performance and characteristics of these methods based on the experimental results of tests using both artificial data and real data.