大数据中电子商务需求信息资源提取仿真

发布时间：2018-05-13 07:41

本文选题：大数据 + 电子商务　；参考：《计算机仿真》2017年07期

【摘要】：对大数据中电子商务需求的信息资源进行提取,可有效的解决庞大数据背景的不确定性。对电子商务的需求信息资源的提取,需要计算出电子商务方案最大化效用,并对其排序,完成信息的提取。传统方法对选取的可行方案进行预测,通过群体多目标选取出资源提取渠道,但并没有对不同方案进行比较筛选,导致提取精度偏低。提出基于区间数的大数据背景下电子商务需求信息资源提取方法。结合误差传递理论对所形成的区间矩阵进行规范化处理,组建电子商务需求信息资源提取多目标规划模型,计算出电子商务需求信息资源提取的理想权重向量,利用多准则提取分析理论计算出电子商务方案最大化群体效用、最小化个体遗憾度以及综合前景值,按照电子商务方案综合前景值的大小排序选取最优电子商务方案。实验结果表明,所提方法能够更真实地描述实际提取过程,且提取可行性和有效性较优。
[Abstract]:Extracting the information resource of the electronic commerce requirement in big data can effectively solve the uncertainty of the huge data background. To extract the information resources of E-commerce, we need to calculate the maximum utility of E-commerce scheme and sort it to complete the extraction of information. The traditional method is used to predict the selected feasible scheme and select the resource extraction channel through the multi-objective population. However, there is no comparison and screening of different schemes, which leads to the low precision of extraction. This paper presents a method for extracting the information resources of e-commerce requirements based on interval number (big data). Combined with error transfer theory, the interval matrix is normalized, and a multi-objective programming model for information resource extraction of electronic commerce is established, and the ideal weight vector of information resource extraction for electronic commerce is calculated. The multi-criteria extraction analysis theory is used to calculate the maximum group utility of e-commerce schemes, to minimize individual regret and comprehensive foreground value, and to select the optimal e-commerce scheme according to the magnitude of the comprehensive foreground value of e-commerce schemes. The experimental results show that the proposed method can describe the actual extraction process more truthfully, and the extraction is feasible and effective.
【作者单位】：凯里学院信息工程学院;凯里学院化学与材料工程学院;
【基金】：贵州省教育厅自科立项项目(黔教合KY字[2016]309)
【分类号】：F724.6;TP311.13

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