关键词: 排序学习, 排序函数, 特征重组, 特征选择

Abstract: Ranking is an essential part of information retrieval.Nowadays there are hundreds of features for constructing ranking functions and it is a hot research topic that how to use these features to construct more efficient ranking functions.So learning to rank，an interdisciplinary field of information retrieval and machine learning，has attracted increasing attention.The construction methods of ranking features show that the features are not independent from each other.However，the state-of-the-art learning to rank approaches merely analyze the features from the aspects of feature recombination and selection for constructing more efficient ranking functions.In this paper，the model structure is proposed.Firstly the features are analysed for constructing the ranking functions.Secondly the features are recombined and selected，and finally ranking functions are learnt through learning to rank methods.And four methods are proposed based on this structure：feature recombination based on principal component analysis，feature selection based on MAP，forward selection and feature selection implied?by learning to rank methods.The experimental results show that ranking functions learned through learning to rank methods based on the feature analysis methods outperform the original ones.

Key words: learning to rank, ranking function, feature recombination, feature selection