摘要
随着现代商业的不断发展,以网络购物为主的各种新兴电子商务平台得到了广泛普及。为了提高不同类型用户需求分析的效率,以实现电子商务平台的精准化客户服务,提出了一种基于数据挖掘和Hadoop平台技术的客户服务管理方法。该方法首先采用权重估计对部署到Hadoop平台的客户进行分类,接着对平台中的商品进行标签化分类并实现用户兴趣商品的参数估计。然后采用Map Reduce并行框架对传统数据关联规则Apriori算法进行了优化,减少数据中冗余的子集。仿真实验结果验证了提出电子商务平台客户精准服务管理方法的可行性,具有较好的准确度和运行效率。
With the continuous development of modern commerce, various emerging e-commerce platforms, including online shopping, have been widely used. In order to improve the efficiency of different types of user needs analysis and achieve accurate customer service of e-commerce platform, a customer service management method based on data mining and Hadoop platform technology is proposed. This method first uses weight estimation to classify customers deployed on the Hadoop platform, then performs the tagged classification of the products in the platform and implements parameter estimation of the user-interested product. Then using the Map Reduce parallel framework to optimize the traditional data association rules Apriori algorithm to reduce redundant subsets of the data. The simulation experiment results verified the feasibility of the customer-precise service management method proposed in the e-commerce platform, and it shows that the proposed method has good accuracy and operating efficiency.
作者
阿荣
王丹琦
Rong A;Dan-qi WANG(Inner Mongolia Technical College of Mechanics and Electrics, Hohhot 010051,China;College of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China)
出处
《机床与液压》
北大核心
2019年第18期153-158,共6页
Machine Tool & Hydraulics
基金
Supported by the Natural Science Foundation of Liaoning Province(2014024011)~~
作者简介
Corresponding author:阿荣,E-mail:1051611654@qq.com.
