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
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
CRF framework for supervised preference aggregation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We present a general treatment of the problem of aggregating preferences from several experts into a consensus ranking, in the context where information about a target ranking is available. Specifically, we describe how such problems can be converted into a standard learning-to-rank one on which existing learning solutions can be invoked. This transformation allows us to optimize the aggregating function for any target IR metric, such as Normalized Discounted Cumulative Gain, or Expected Reciprocal Rank. When applied to crowdsourcing and meta-search benchmarks, our new algorithm improves on state-of-the-art preference aggregation methods.