摘要
随着互联网的发展和用户的增长,广告行业从传统的线下广告模式,逐步转变为线上广告模式.同时,由于大数据分析技术的运用,线上广告模式相比于传统广告也体现了巨大的优越性.广告主之间相互竞争,通过竞价的方式,将自己的广告投放在运营媒体的广告位上.所以,在投放前预测该广告可能被用户点击的概率(CTR),对于广告主减少成本和增加可能收入来说非常重要.本文在调研了目前常用的广告点击率预测模型的基础上,选取广告主、广告和投放媒体平台信息作为预测模型的特征,采用真实数据集验证说明各种模型的优劣性,以及不同特征对广告点击率预测结果的影响.
With the development of the Internet and the growth of users, the advertising industry originated from the traditional offiine advertising model, is gradually transforming into online advertising model. At the same time, due to the use of large data analysis technology, online advertising shows great advantages when compared with traditional advertising. The advertisers deliver their advertisements to the platform＇s specific positions by competition auction of counterparts. Therefore, it is important to predict the click through rate（CTR） of a given advertisement before auction, which is important for advertisers to reduce costs and expand their likely revenue.This paper introduces the commonly used ad click rate prediction model, uses the information from different advertisers, advertisements and media platforms as the features of machine learning, and uses real data sets to illustrate the advantages of various models,and the impact of different features on the ad click rate.
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第5期80-86,100,共8页
Journal of East China Normal University(Natural Science)
基金
国家重点研发计划(2016YFB1000905)
国家自然科学基金广东省联合重点项目(U1401256)
国家自然科学基金(61672234
61402177)
华东师范大学信息化软课题
关键词
计算广告
CTR
机器学习
computational advertising
CTR
machine learning
