Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth 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
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
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
IntervalRank: isotonic regression with listwise and pairwise constraints
Proceedings of the third ACM international conference on Web search and data mining
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There have been great needs to develop effective methods for combining multiple rankings from heterogeneous domains into one single rank list arising from many recent web search applications, such as integrating web search results from multiple engines, facets, or verticals. We define this problem as Learning to blend rankings from multiple domains. We propose a class of learning-to-blend methods that learn a monotonically increasing transformation for each ranking so that the rank order in each domain is preserved and the transformed values are comparable across multiple rankings. The transformation learning can be tackled by solving a quadratic programming problem. The novel machine learning method for blending multiple ranking lists is evaluated with queries sampled from a commercial search engine and a promising improvement of Discounted Cumulative Gain has been observed.