Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Linear discriminant model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Fast learning of document ranking functions with the committee perceptron
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Here or there: preference judgments for relevance
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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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. 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 we study the effects of outlying pairs in rank learning with pairwise preferences and introduce a new meta-learning algorithm capable of suppressing these undesirable effects. This algorithm works as a second optimization step in which any linear baseline ranker can be used as input. Experiments on eight different ranking datasets show that this optimization step produces statistically significant performance gains over state-of-the-art methods.