摘要
随着互联网的普及和大数据技术的应用,互联网平台的各种用户信息被关注起来,利用大数据推荐算法,根据用户的网络痕迹分析用户消费行为、描绘出用户画像,掌握用户的行为习惯和个人喜好,为用户推送个性化的产品或服务信息。依托于大数据算法推荐的信息流广告以其投放精准和转化效率高,越来越受到广告主与用户的认可,已经成为主流的互联网广告模式。大数据技术和广告内容决定了信息流广告的传播效果。本文主要研究大数据算法推荐背景下信息流广告在内容方面的提升路径,即信息流广告内驱型提升路径,为提高信息流广告的投放效果提供相关的理论支撑。
With the popularization of the Internet and the application of big data technology,various user information on internet platforms has been paid attention to,mainly through the use of big data recommendation algorithms to analyze user consumption behavior based on user network traces,depict user profiles,master user behavior habits and personal preferences,and push personalized product or service information for users.The information flow advertising recommended by big data algorithms is increasingly recognized by advertisers and users due to its precise placement and high conversion efficiency,and has become the mainstream Internet advertising mode.Big data technology and advertising content determine the dissemination effect of information flow advertising.This paper mainly studies the improvement path of information flow advertising in terms of content under the background of big data algorithm recommendation,namely the internal drive type improvement path of information flow advertising,providing relevant theoretical support for improving the placement effect of information flow advertising.
作者
胡雪松
孙增军
HU Xuesong;SUN Zengjun(Zhengzhou University of Economics and Trade,Zhengzhou 450000,China;South Seoul University,Cheonan 31020,South Korea)
出处
《科技创新与生产力》
2023年第9期94-97,101,共5页
Sci-tech Innovation and Productivity
关键词
算法推荐
信息流广告
内驱型
KOL
algorithm recommendation
information flow advertising
internal drive type
KOL
作者简介
胡雪松(1982-),男,安徽池州人,在读博士,副教授,主要从事管理科学与网络营销研究,E-mail:531722007@qq.com;通信作者:孙增军(1982-),男,山东临沂人,教授、博士生导师,主要从事管理科学与网络营销研究,E-mail:s1982@nsu.ac.kr。
