在线广告中点击率预测研究

肖垚; 毕军芳; 韩易; 董启文

doi:10.3969/j.issn.1000-5641.2017.05.008

在线广告中点击率预测研究

doi: 10.3969/j.issn.1000-5641.2017.05.008

肖垚¹,
毕军芳²,
韩易¹,
董启文^1, ,

1.
华东师范大学数据科学与工程学院, 上海 200062
2.
长江口水文水资源勘测局, 上海 200136

基金项目:

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

国家自然科学基金 61402177

详细信息

作者简介:
肖垚, 男, 硕士研究生, 研究方向为广告点击率预测

通讯作者:
董启文, 男, 硕士生导师, 副教授, 研究方向为网络信息学、生物信息学.E-mail:qwdong@dase.ecnu.edu.cn

中图分类号: TP391
计量
- 文章访问数: 235
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- PDF下载量: 349
- 被引次数: 0
出版历程
- 收稿日期: 2017-05-01
- 刊出日期: 2017-09-25

Study of click through rate prediction in online advertisement

1.
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
2.
Yangtze River Estuary Survey Bureau of Hydrology and Water Resource, CWRC, Ministry of Water Resources, Shanghai 200136, China

摘要

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.
- 计算广告 /
- CTR /
- 机器学习
Abstract: With the development of the Internet and the growth of users, the advertising industry originated from the traditional offline 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.
- computational advertising /
- CTR /
- machine learning

HTML全文

图 1 不同正负样本比例对模型的影响

Fig. 1 Influence of Different Positive and Negative Sample Proportions on Model

下载: 全尺寸图片幻灯片

图 2 不同模型预测性能的对比

Fig. 2 Comparison of Predictive Performance of Different Models

下载: 全尺寸图片幻灯片

表 1 日志字段对应的特征

Tab. 1 The log field corresponding to the feature

特征类型	日志字段
一天中的时间段	tis
地域	ip
竞价平台	adx
流量类型	devicetype
平台	platform
浏览器	browser
操作系统	os
广告位ID	adslot_id
广告位位置	adslot_pos
活动ID	campaign_id
活动组ID	group_id
素材ID	creative_id
素材尺寸	creative_size
广告主ID	advertiser_id
广告代理ID	ad_agent_id

下载: 导出CSV

表 2 不同特征对模型预测结果的影响

Tab. 2 Impact of Different Features on Model Prediction

特征评价指标	广告信息	广告信息+用户信息	广告信息+用户信息+媒体平台信息
Precision	0.705 4	0.764 4	0.774 4
Auc	0.704 9	0.830 3	0.836 9
Logloss	0.598 5	0.480 8	0.474 9

下载: 导出CSV

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