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。
An extraction method of information resource required by electronic commerce under big data back- ground is proposed based on interval number. The normalized process for generated interval matrix integrated with propagation of error is carried out, and a multi-objective programming model of the extraction of information resource is built. Then the ideal weight vector of the extraction is worked out. In addition, the multi-criteria extraction analy- sis theory is used to work out the maximization group effectiveness of electronic commerce project, degree of minimi- zation individual regret, and comprehensive prospect value. Finally, the optimal scheme of electronic commerce is se- lected according to rank of value of comprehensive prospect. Experimental results show that the method can factually describe actual extraction process. It has good extraction feasibility and validity.
出处
《计算机仿真》
北大核心
2017年第7期298-301,368,共5页
Computer Simulation
基金
贵州省教育厅自科立项项目(黔教合KY字[2016]309)
关键词
大数据
电子商务
需求信息资源提取
Big data
Electronic Commerce
Extraction of required information resource
