摘要
在电子商务环境中,精确理解用户的兴趣,提供个性化商品推荐服务成为各大电商平台关注的热点。通过对实时获取的人脸特征数据进行归一化处理,计算出用户对商品的评分和喜爱程度;再利用机器学习算法建立并修正"商品—人脸"兴趣模型库;最后通过用户对商品的喜爱程度来实现商品的个性化推荐,并使用MVC框架实现了基于人脸识别的商品推荐系统。
In the e-commerce environment, accurately understanding the interests of users and providing personalized productrecommendation services have become the hotspots of major e-commerce platforms. This paper normalizes the face feature dataobtained in real time to calculate the user＇s rating and preference for the product; then uses the machine learning algorithm toestablish and correct the ＂commodity-face＂ interest model library; finally, achieves personalized recommendation of the product bythe user＇s preference for the product, and uses the MVC framework to implement a product recommendation system based on facerecognition.
作者
陈果
周志锋
杨小波
王成
欧阳纯萍
Chen Guo;Zhou Zhifeng;Yang Xiaobo;Wang Cheng;Ouyang Chunping(Computer School/Software School,University of South China,Hengyang,Hunan 421001,China)
出处
《计算机时代》
2018年第11期52-55,共4页
Computer Era
基金
湖南省大学生研究性学习和创新性实验计划项目(湘教通[2017]205号-350)
关键词
人脸识别
商品推荐
个性化推荐
推荐系统
face recognition
product recommendation
personalized recommendation
recommendation system
