吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (2): 558-563.doi: 10.13229/j.cnki.jdxbgxb20170015

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Information push model-building based on maximum mutual information coefficient

TAN Si-qiao1, 2, ZHANG Xi2, 3, LI Qian2, AI Chen4   

  1. 1.School of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China;
    2.Hunan Engineer Research Center for Information Technology in Agriculture, Changsha 410128, China;
    3.College of Plant Protection, Hunan Agricultural University, Changsha 410128, China;
    4.College of Medicine, Shaoyang University, Shaoyang 422000, China
  • Received:2017-01-05 Online:2018-03-01 Published:2018-03-01

Abstract: The correlation indicator, Maximum Information Coefficient (MIF), which can pervasively measure the nonlinear relationship, is introduced to solve the problem of inaccurate selection of the near-neighbor set of the target users. The indicator is employed to measure the similarity between users. First, the near-neighbor set target users is selected based on a given threshold. Then, the personalized SVM prediction model is built with the attained near-neighbor set as the training set to carried out scoring prediction for the interesting items of the target users. Simulation results show that the near-neighbor set selected by the MIF Measuring is more accurate than that selected by the Pearson Measuring, and has the merit of insensitive to the threshold.

Key words: computer application, information push, similarity measure, model-building, maximum mutual information coefficient

CLC Number: 

  • TP391
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