Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Feature selection for ranking using boosted trees
Proceedings of the 18th ACM conference on Information and knowledge management
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
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Ranking is an essential part of information retrieval(IR) tasks such as Web search. Nowadays there are hundreds of features for ranking. So learning to rank(LTR), an interdisciplinary field of IR and machine learning(ML), has attracted increasing attention. Those features used in the IR are not always independent from each other, hence the feature selection, an important issue in ML, should be paid attention to for LTR. However, the state-of-the-art LTR approaches merely analyze the connection among the features from the aspects of feature selection. In this paper, we propose a hierarchical feature selection strategy containing 2 phases for ranking and learn ranking functions. The experimental results show that ranking functions based on the selected feature subset significantly outperform the ones based on all features.