Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance score normalization for metasearch
Proceedings of the tenth international conference on Information and knowledge management
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Score Distributions in Information Retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Tablerank: a ranking algorithm for table search and retrieval
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A signal-to-noise approach to score normalization
Proceedings of the 18th ACM conference on Information and knowledge management
Modeling score distributions in information retrieval
Information Retrieval
Score transformation in linear combination for multi-criteria relevance ranking
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Explicit relevance models in intent-oriented information retrieval diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Ranking Algorithm for Semantic Document Annotations
International Journal of Information Retrieval Research
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Understanding Similarity Metrics in Neighbour-based Recommender Systems
Proceedings of the 2013 Conference on the Theory of Information Retrieval
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Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless deviations consistently occurring in the input score distributions, which distort the combined result when the individual biases differ from each other. We propose a score-based rank aggregation model where the source scores are normalized to a common distribution before being combined. Early experiments on available data from several TREC collections are shown to support our proposal.