基于最大互信息系数的信息推送模型构建

doi:10.13229/j.cnki.jdxbgxb20170015

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

基于最大互信息系数的信息推送模型构建

谭泗桥^{1, 2}, 张席^{2, 3}, 李钎², 艾陈⁴

1.湖南农业大学信息科学技术学院,长沙 410128;
2.湖南省农村农业信息化工程技术研究中心,长沙410128;
3.湖南农业大学植物保护学院,长沙 410128;
4.邵阳学院医学院,湖南邵阳 422000

收稿日期:2017-01-05 出版日期:2018-03-01 发布日期:2018-03-01
通讯作者: 艾陈(1986-),男,讲师. 研究方向:大数据分析.E-mail:33979639@qq.com
作者简介:谭泗桥(1974-),男,副教授,博士. 研究方向:模式识别及预测. E-mail:tsq@hunau.edu.cn
基金资助:
国家自然科学基金项目(31772157); 国家星火计划项目(2014GA770015)

Information push model-building based on maximum mutual information coefficient

TAN Si-qiao^{1, 2}, ZHANG Xi^{2, 3}, LI Qian², AI Chen⁴

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

摘要/Abstract

摘要： 针对目标用户近邻集合选择失准的问题,引入可普适性测度非线性关系的关联指标——最大互信息系数(MIC),并以此测度用户间的相似程度。基于某一给定的阈值,为目标用户选择近邻集合,然后以近邻集合作为训练集,构建支持向量机个性化预测模型,对目标用户的感兴趣项目进行打分预测。仿真结果表明,MIC测度较Pearson等测度选择的近邻集合更为准确,并具有对阈值不敏感的优点。

关键词: 计算机应用, 信息推送, 相似性测度, 模型构建, 最大互信息系数

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

中图分类号:

TP391

谭泗桥, 张席, 李钎, 艾陈. 基于最大互信息系数的信息推送模型构建[J]. 吉林大学学报(工学版), 2018, 48(2): 558-563.

TAN Si-qiao, ZHANG Xi, LI Qian, AI Chen. Information push model-building based on maximum mutual information coefficient[J]. 吉林大学学报(工学版), 2018, 48(2): 558-563.

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基于最大互信息系数的信息推送模型构建

Information push model-building based on maximum mutual information coefficient

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