Noise Correction in Pairwise Document Preferences for Learning to Rank

@inproceedings{Trivedi2016NoiseCI,
  title={Noise Correction in Pairwise Document Preferences for Learning to Rank},
  author={H. Trivedi and Prasenjit Majumder},
  booktitle={AIRS},
  year={2016}
}
This paper proposes a way of correcting noise in the training data for Learning to Rank. It is natural to assume that some level of noise might seep in during the process of producing query-document relevance labels by human evaluators. These relevance labels, which act as gold standard training data for Learning to Rank can adversely affect the efficiency of learning algorithm if they contain errors. Hence, an automated way of reducing noise can be of great advantage. The focus in this paper… Expand

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