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Many of the recently proposed algorithms for learning feature-based ranking functions are based on the pairwise preference framework, in which instead of taking documents in isolation, document pairs are used as instances in the learning process [3, 5] . One disadvantage of this process is that a noisy relevance judgment on a single document can lead to a large number of mis-labeled document pairs. This can jeopardize robustness and deteriorate overall ranking performance. In this paper wedoi:10.1145/1458082.1458348 dblp:conf/cikm/CarvalhoECC08 fatcat:4f56irgtevgzpocqh5ceg7fycm