摘要
电子商务商品推荐系统可以帮助用户从海量的商品中快速找到自己意向的商品,针对当前电子商务商品智能推荐系统存在工作效率低、推荐误差大等缺陷,设计了基于云平台的电子商务商品智能推荐系统。首先,分析当前电子商务商品推荐系统的研究现状,找到引起推荐效果差的原因;然后,根据云平台的数据处理技术搭建电子商务商品推荐系统,并对电子商务商品推荐系统的关键技术进行设计;最后进行云平台的电子商务商品智能推荐系统的仿真测试。测试结果表明,所提系统克服了传统电子商务商品推荐系统存在的不足,加快了用户从海量商品中找出自己真正需要商品的速度,提高了电子商务商品推荐精度,且电子商务商品推荐误差远远低于传统电子商务商品推荐系统,具有更高的实际应用价值。
The e-commerce commodity recommendation system can help users quickly find their ideal ones from the mass of commodities.In view of the deficiencies of low work efficiency and great error in the current e-commerce commodity intelligent recommendation system,an e-commerce commodity intelligent recommendation system based on cloud platform is designed.Firstly,the current research status of e-commerce commodity recommendation system is analyzed to find the reasons for poor recommendation effect.Secondly,the e-commerce commodity recommendation system is built according to the data processing technology of cloud platform,and the key technologies of e-commerce commodity recommendation system are designed.Finally,the simulation test of e-commerce commodity intelligent recommendation system based on cloud platform is performed.The test results show that the proposed system overcomes the deficiencies of the traditional e-commerce commodity recommendation system,so that users can find the commodities they really need from the mass of commodities more quickly.In addition,it improves the accuracy of e-commerce commodity recommendation,so that the error rate of e-commerce commodity recommendation is much lower than that of the traditional e-commerce commodity recommendation system.Therefore,the proposed system is of higher practical application value.
作者
黄玲
余霞
HUANG Ling;YU Xia(Wuhan Institute of Design and Sciences,Wuhan 430205,China)
出处
《现代电子技术》
北大核心
2020年第5期183-186,共4页
Modern Electronics Technique
基金
湖北省教育厅科研计划项目(B201426)
校级优质课程《品牌管理》建设(2017YK110)。
关键词
电子商务商品
智能推荐系统
云平台
商品特征
商品相似度
商品数据集
用户偏好
e-commerce commodity
intelligent recommendation system
cloud platform
commodity feature
commodity similarity
commodity data set
user preference
