Computer Science ›› 2017, Vol. 44 ›› Issue (12): 255-259.doi: 10.11896/j.issn.1002-137X.2017.12.046

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Feature Construction Method for Learning to Rank Based on Optimization of Matrix Factorization

YANG Xiao, CUI Chao-ran and WANG Shuai-qiang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Feature selection can improve ranking efficiency and accuracy.Current study mainly prefers selecting the most distinguishing feature set rather than feature construction,where the selection is mostly according to the significance of features and the similarity between features.Since the features are mostly induced manually,there are inevitably overlap and redundancy between them.In order to reduce the redundancy,matrix decomposition is used to generate new features set.An optimization algorithm was designed according to the effect of the feature matrix decomposed,and the gap between the decomposed feature matrix and the original matrix.Then a matrix decomposition based optimization for learning to rank,which named by MFRank,was proposed to take into account,the ranking result acquired by the features,which cannot be handled by matrix decomposition method such as singular value decomposition (SVD),etc.A stochastic projective sub-gradient algorithm was used in experiments to obtain the approximate optimal values for the optimization problems,and experimental result on MQ2008,which is an open test set,shows that the proposed MFRank algorithm can obtain comparative result as RankBoost,RankSVM-Struct which are the state-of-the-art algorithms.

Key words: Feature construction,Learning to rank,Matrix factorization,Optimization

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