IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Proceedings of the 16th international conference on World Wide Web
Unsupervised rank aggregation with distance-based models
Proceedings of the 25th international conference on Machine learning
Bayesian inference for Plackett-Luce ranking models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
Rank aggregation via nuclear norm minimization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise ranking aggregation in a crowdsourced setting
Proceedings of the sixth ACM international conference on Web search and data mining
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Many areas of study, such as information retrieval, collaborative filtering, and social choice face the preference aggregation problem, in which multiple preferences over objects must be combined into a consensus ranking. Preferences over items can be expressed in a variety of forms, which makes the aggregation problem difficult. In this work we formulate a flexible probabilistic model over pairwise comparisons that can accommodate all these forms. Inference in the model is very fast, making it applicable to problems with hundreds of thousands of preferences. Experiments on benchmark datasets demonstrate superior performance to existing methods