摘要
计算广告是根据给定的用户和网页内容,通过计算得到与之最匹配的广告并进行精准定向投放的一种广告投放机制.广告的点击率预测是指利用点击日志预测的点击率,其结果受到广告的自身性质、广告位置、页面信息、用户性质,以及广告主信誉等诸多因素的影响.有效地预测广告的点击率,对于提高广告投放的效率有着至关重要的作用.本文介绍了广告点击率预测的常用模型,包括历史数据丰富的广告点击率预测模型、新广告和稀疏广告的点击率估算模型和点击率预测的优化模型,并通过真实数据集举例说明了其实现的方法.
Computational advertising is a kind of advertising mechanism which has the capability to find the most suitable ads for given users and web content, so as to advertises them accurately. Therefore, estimating click-through rate (CTR) precisely makes significant difference in the efficiency of advertising on the Internet. Ad click-through rate prediction is to estimate CTR with click log, which is influenced by the nature features of ad, the position, the page information, user properties, the reputation of advertisers and such other factors. This paper is aimed to illus- trate useful CTR prediction models, including CTR models for ads of abundant history data, CTR models for rare ads or new ads and some optimization models. Finally, the implementation methods with real data set were demonstrated as examples.
出处
《华东师范大学学报(自然科学版)》
CAS
CSCD
北大核心
2013年第3期2-14,共13页
Journal of East China Normal University(Natural Science)
基金
工信部核高基项目(2010ZX01042-002-003-004)
国家自然科学基金重点项目(61033007)
国家973课题(2010CB328106)
教育部新世纪人才支撑计划(NCET-10-0388)
创新研究群体科学基金(61021004)
关键词
计算广告
点击率估算
逻辑回归模型
贝叶斯方法
computational advertisement
dick-through rate
logistic regression
Bayes method