Predicting the performance of linearly combined IR systems
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Rank aggregation for meta-search engines
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Performance prediction of data fusion for information retrieval
Information Processing and Management: an International Journal
Proceedings of the 16th international conference on World Wide Web
An outranking approach for rank aggregation in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An Unsupervised Learning Algorithm for Rank Aggregation
ECML '07 Proceedings of the 18th European conference on Machine Learning
Journal of Artificial Intelligence Research
Data fusion with correlation weights
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Semi-supervised learning to rank with preference regularization
Proceedings of the 20th ACM international conference on Information and knowledge management
Information Sciences: an International Journal
CRF framework for supervised preference aggregation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Ranking aggregation is a task of combining multiple ranking lists given by several experts or simple rankers to get a hopefully better ranking. It is applicable in several fields such as meta search and collaborative filtering. Most of the existing work is under an unsupervised framework. In these methods, the performances are usually limited especially in unreliable case since labeled information is not involved in. In this paper, we propose a semi-supervised ranking aggregation method, in which preference constraints of several item pairs are given. In our method, the aggregation function is learned based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data should be consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experimental results on toy data and the OHSUMED data are presented to illustrate the validity of our method.