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.
['Guichun Hua', 'Min Zhang', 'Yiqun Liu', 'Shaoping Ma', 'Hang Yin']
Construct weak ranking functions for learning linear ranking function