摘要
伴随着互联网技术的快速发展和应用拓展,三网(因特网、电信网、广播电视网)融合为传统广播电视媒介带来了发展机遇。但随着数据规模的增长,现有推荐算法对多“目录”广播电视用户精准推荐的效果并未达到预期要求,具有较为明显的不足。本文针对用户之间的相似关系和产品之间的相似度,分别用皮尔逊相关系数、基于TF-IDF的余弦相似度与协同推荐构建了2种可以对新型广播电视用户精准推荐的算法流程,并能够得到产品的准确分类与精准投放。
With the rapid development of Internet technology and application expansion,triple play(Internet,telecommunication network,broadcast TV network)has brought development opportunities for traditional broadcast TV media.However,with the increase of data size,the existing recommendation algorithm does not meet the expected requirements for the accurate recommendation of many“category”broadcast TV users,and has obvious deficiencies.In this paper,based on the similarity between users and the similarity between products,Pearson correlation coefficient,TF-IDF-based cosine similarity and collaborative recommendation are used to construct two algorithm flows that can be accurately recommended for new broadcast TV users,and can get accurate classification and accurate delivery of products.
作者
谢浩然
卫巍
杨志辉
邓居智
葛坤朋
XIE Hao-ran;WEI Wei;YANG Zhi-hui;DENG Ju-zhi;GE Kun-peng(School of Geophysics&Measurement-control Technology,East China University of Technology,Nanchang 330013,China;School of Chemical Biology&Materials Science,East China University of Technology,Nanchang 330013,China;School of Science,East China University of Technology,Nanchang 330013,China)
出处
《计算机与现代化》
2019年第9期65-71,共7页
Computer and Modernization
关键词
协同过滤
推荐系统
TF-IDF
新型广播电视节目
余弦相似度
collaborative filtering
recommendation system
TF-IDF
new radio and television program
cosine similarity