摘要
传统个性化推荐应用存在与用户实际需求信息匹配度低的问题,为此设计了一种电子商务网站用户体验信息个性化推荐方法。通过分析数据清洗工作流程,设计用户体验信息个性化推荐应用算法,并根据用户在网站上的相关行为,计算用户个性推荐项目相似度。整合用户对个性化推荐的反馈数据,提供用户在网站体验的个性化信息,以此完成对用户体验信息个性化推荐。此外,采用设计仿真实验的方式,验证了用户经过提出的个性化推荐后,在电子商务网站的平均浏览时长增多,证明推荐的个性化内容与用户的实际信息需求匹配度较高。
Traditional personalized recommendation applications have the problem of low matching with the actual needs of users,so a personalized recommendation method for user experience information of e-commerce websites is designed.By analyzing the work flow of data cleaning,the personalized recommendation application algorithm of user experience information is designed,and the similarity of user personality recommendation project is calculated according to the relevant behavior of users on the website.Integrate the feedback data of users on personalized recommendation,and provide personalized information of user experience on the website,so as to complete the personalized recommendation of user experience information.In addition,the design simulation experiment is used to verify that after the personalized recommendation,the average browsing time of the e-commerce website increases,which proves that the recommended personalized content matches the actual information needs of users.
出处
《科技创新与应用》
2020年第27期184-185,共2页
Technology Innovation and Application
关键词
电子商务网站
用户体验信息
个性化推荐
应用
e-commerce website
user experience information
personalized recommendation
application
