IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
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Many Learning to Rank models, which apply machine learning techniques to fuse weak ranking functions and enhance ranking performances, have been proposed for web search. However, most of the existing approaches only apply the Min --- Max normalization method to construct the weak ranking functions without considering the differences among the ranking features. Ranking features, such as the content-based feature BM 25 and link-based feature PageRank , are different from each other in many aspects. And it is unappropriate to apply an uniform method to construct weak ranking functions from ranking features. In this paper, comparing the three frequently used normalization methods: Min --- Max , Log , Arctan normalization, we analyze the differences among three normalization methods when constructing the weak ranking functions, and propose two normalization selection methods to decide which normalization should be used for a specific ranking feature. The experimental results show that the final ranking functions based on normalization selection methods significantly outperform the original one.